{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f18e3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f10b098",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
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       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
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       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
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       "      <td>2011-01-01 04:00:00</td>\n",
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       "      <td>2012-12-19 19:00:00</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "      <th>10882</th>\n",
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       "      <td>4</td>\n",
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       "      <td>57</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>10</td>\n",
       "      <td>231</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10883</th>\n",
       "      <td>2012-12-19 21:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>61</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>4</td>\n",
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       "      <td>168</td>\n",
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       "    <tr>\n",
       "      <th>10884</th>\n",
       "      <td>2012-12-19 22:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>66</td>\n",
       "      <td>8.9981</td>\n",
       "      <td>4</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10886 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  season  holiday  workingday  weather   temp  \\\n",
       "0      2011-01-01 00:00:00       1        0           0        1   9.84   \n",
       "1      2011-01-01 01:00:00       1        0           0        1   9.02   \n",
       "2      2011-01-01 02:00:00       1        0           0        1   9.02   \n",
       "3      2011-01-01 03:00:00       1        0           0        1   9.84   \n",
       "4      2011-01-01 04:00:00       1        0           0        1   9.84   \n",
       "...                    ...     ...      ...         ...      ...    ...   \n",
       "10881  2012-12-19 19:00:00       4        0           1        1  15.58   \n",
       "10882  2012-12-19 20:00:00       4        0           1        1  14.76   \n",
       "10883  2012-12-19 21:00:00       4        0           1        1  13.94   \n",
       "10884  2012-12-19 22:00:00       4        0           1        1  13.94   \n",
       "10885  2012-12-19 23:00:00       4        0           1        1  13.12   \n",
       "\n",
       "        atemp  humidity  windspeed  casual  registered  count  \n",
       "0      14.395        81     0.0000       3          13     16  \n",
       "1      13.635        80     0.0000       8          32     40  \n",
       "2      13.635        80     0.0000       5          27     32  \n",
       "3      14.395        75     0.0000       3          10     13  \n",
       "4      14.395        75     0.0000       0           1      1  \n",
       "...       ...       ...        ...     ...         ...    ...  \n",
       "10881  19.695        50    26.0027       7         329    336  \n",
       "10882  17.425        57    15.0013      10         231    241  \n",
       "10883  15.910        61    15.0013       4         164    168  \n",
       "10884  17.425        61     6.0032      12         117    129  \n",
       "10885  16.665        66     8.9981       4          84     88  \n",
       "\n",
       "[10886 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据\n",
    "df = pd.read_csv(\"C:\\\\Users\\\\34901\\\\Desktop\\\\学习\\\\数据可视化Python\\\\数据可视化python\\\\数据可视化python\\\\04课堂源码\\\\第09章 共享单车使用数量预测\\\\train.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d85c1c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
      "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
      "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
      "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
      "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
      "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
      "\n",
      "   humidity  windspeed  casual  registered  count  \n",
      "0        81        0.0       3          13     16  \n",
      "1        80        0.0       8          32     40  \n",
      "2        80        0.0       5          27     32  \n",
      "3        75        0.0       3          10     13  \n",
      "4        75        0.0       0           1      1  \n"
     ]
    }
   ],
   "source": [
    "#一。数据检视\n",
    "#1.查看前5条数据\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76fccad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   datetime    10886 non-null  object \n",
      " 1   season      10886 non-null  int64  \n",
      " 2   holiday     10886 non-null  int64  \n",
      " 3   workingday  10886 non-null  int64  \n",
      " 4   weather     10886 non-null  int64  \n",
      " 5   temp        10886 non-null  float64\n",
      " 6   atemp       10886 non-null  float64\n",
      " 7   humidity    10886 non-null  int64  \n",
      " 8   windspeed   10886 non-null  float64\n",
      " 9   casual      10886 non-null  int64  \n",
      " 10  registered  10886 non-null  int64  \n",
      " 11  count       10886 non-null  int64  \n",
      "dtypes: float64(3), int64(8), object(1)\n",
      "memory usage: 1020.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#2.查看确缺失值数量\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa47ab23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             season       holiday    workingday       weather         temp  \\\n",
      "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.00000   \n",
      "mean       2.506614      0.028569      0.680875      1.418427     20.23086   \n",
      "std        1.116174      0.166599      0.466159      0.633839      7.79159   \n",
      "min        1.000000      0.000000      0.000000      1.000000      0.82000   \n",
      "25%        2.000000      0.000000      0.000000      1.000000     13.94000   \n",
      "50%        3.000000      0.000000      1.000000      1.000000     20.50000   \n",
      "75%        4.000000      0.000000      1.000000      2.000000     26.24000   \n",
      "max        4.000000      1.000000      1.000000      4.000000     41.00000   \n",
      "\n",
      "              atemp      humidity     windspeed        casual    registered  \\\n",
      "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.000000   \n",
      "mean      23.655084     61.886460     12.799395     36.021955    155.552177   \n",
      "std        8.474601     19.245033      8.164537     49.960477    151.039033   \n",
      "min        0.760000      0.000000      0.000000      0.000000      0.000000   \n",
      "25%       16.665000     47.000000      7.001500      4.000000     36.000000   \n",
      "50%       24.240000     62.000000     12.998000     17.000000    118.000000   \n",
      "75%       31.060000     77.000000     16.997900     49.000000    222.000000   \n",
      "max       45.455000    100.000000     56.996900    367.000000    886.000000   \n",
      "\n",
      "              count  \n",
      "count  10886.000000  \n",
      "mean     191.574132  \n",
      "std      181.144454  \n",
      "min        1.000000  \n",
      "25%       42.000000  \n",
      "50%      145.000000  \n",
      "75%      284.000000  \n",
      "max      977.000000  \n"
     ]
    }
   ],
   "source": [
    "#3.数据分布\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a2aa4c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#二。切分 训练集和 测试集，\n",
    "# 并且增加一列 trainortest ，\n",
    "# 来区分每条数据 是 属于训练集 还是测试集合，\n",
    "#然后合并\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a3c2762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 12)\n",
      "Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',\n",
      "       'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#切分\n",
    "print(df.shape)\n",
    "print(df.columns)\n",
    "train_df,test_df=train_test_split(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "201b3207",
   "metadata": {},
   "outputs": [
    {
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       "      <td>17.425</td>\n",
       "      <td>81</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>5</td>\n",
       "      <td>123</td>\n",
       "      <td>128</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5632</th>\n",
       "      <td>2012-01-09 19:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8.20</td>\n",
       "      <td>11.365</td>\n",
       "      <td>93</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>3</td>\n",
       "      <td>187</td>\n",
       "      <td>190</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5250</th>\n",
       "      <td>2011-12-12 20:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.30</td>\n",
       "      <td>15.910</td>\n",
       "      <td>56</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>8</td>\n",
       "      <td>157</td>\n",
       "      <td>165</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3967</th>\n",
       "      <td>2011-09-16 08:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>67</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>23</td>\n",
       "      <td>386</td>\n",
       "      <td>409</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2011-01-04 13:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>18</td>\n",
       "      <td>79</td>\n",
       "      <td>97</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8164 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime  season  holiday  workingday  weather   temp  \\\n",
       "3236  2011-08-04 18:00:00       3        0           1        1  28.70   \n",
       "1067  2011-03-09 00:00:00       1        0           1        1  10.66   \n",
       "3245  2011-08-05 03:00:00       3        0           1        1  26.24   \n",
       "2300  2011-06-03 18:00:00       2        0           1        1  28.70   \n",
       "4716  2011-11-09 14:00:00       4        0           1        1  21.32   \n",
       "...                   ...     ...      ...         ...      ...    ...   \n",
       "6371  2012-03-02 17:00:00       1        0           1        3  14.76   \n",
       "5632  2012-01-09 19:00:00       1        0           1        3   8.20   \n",
       "5250  2011-12-12 20:00:00       4        0           1        1  12.30   \n",
       "3967  2011-09-16 08:00:00       3        0           1        1  17.22   \n",
       "81    2011-01-04 13:00:00       1        0           1        1   9.84   \n",
       "\n",
       "       atemp  humidity  windspeed  casual  registered  count trainortest  \n",
       "3236  33.335        74    19.9995      88         435    523       train  \n",
       "1067  14.395        65     6.0032       0           9      9       train  \n",
       "3245  29.545        78     6.0032       0           6      6       train  \n",
       "2300  31.820        24    19.9995      76         488    564       train  \n",
       "4716  25.000        48    11.0014      26         153    179       train  \n",
       "...      ...       ...        ...     ...         ...    ...         ...  \n",
       "6371  17.425        81    15.0013       5         123    128       train  \n",
       "5632  11.365        93     6.0032       3         187    190       train  \n",
       "5250  15.910        56     6.0032       8         157    165       train  \n",
       "3967  21.210        67    15.0013      23         386    409       train  \n",
       "81    11.365        56    12.9980      18          79     97       train  \n",
       "\n",
       "[8164 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#添加列\n",
    "train_df['trainortest']='train'\n",
    "test_df['trainortest']='test'\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aaa653d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>trainortest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3839</th>\n",
       "      <td>2011-09-10 23:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>25.42</td>\n",
       "      <td>28.030</td>\n",
       "      <td>88</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>32</td>\n",
       "      <td>107</td>\n",
       "      <td>139</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5605</th>\n",
       "      <td>2012-01-08 16:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.40</td>\n",
       "      <td>20.455</td>\n",
       "      <td>37</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>58</td>\n",
       "      <td>256</td>\n",
       "      <td>314</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4698</th>\n",
       "      <td>2011-11-08 20:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.40</td>\n",
       "      <td>20.455</td>\n",
       "      <td>76</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>12</td>\n",
       "      <td>169</td>\n",
       "      <td>181</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>2012-01-12 14:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>77</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>24</td>\n",
       "      <td>132</td>\n",
       "      <td>156</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>2011-02-06 05:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>60</td>\n",
       "      <td>15.0013</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>2012-11-01 08:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14.76</td>\n",
       "      <td>18.940</td>\n",
       "      <td>56</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>12</td>\n",
       "      <td>668</td>\n",
       "      <td>680</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6157</th>\n",
       "      <td>2012-02-12 19:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.56</td>\n",
       "      <td>6.820</td>\n",
       "      <td>40</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10221</th>\n",
       "      <td>2012-11-11 07:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.30</td>\n",
       "      <td>15.910</td>\n",
       "      <td>89</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>68</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>2011-03-07 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.20</td>\n",
       "      <td>9.090</td>\n",
       "      <td>75</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2011-01-08 07:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.56</td>\n",
       "      <td>9.090</td>\n",
       "      <td>74</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2722 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime  season  holiday  workingday  weather   temp  \\\n",
       "3839   2011-09-10 23:00:00       3        0           0        2  25.42   \n",
       "5605   2012-01-08 16:00:00       1        0           0        1  16.40   \n",
       "4698   2011-11-08 20:00:00       4        0           1        1  16.40   \n",
       "5698   2012-01-12 14:00:00       1        0           1        1  17.22   \n",
       "553    2011-02-06 05:00:00       1        0           0        1  10.66   \n",
       "...                    ...     ...      ...         ...      ...    ...   \n",
       "9983   2012-11-01 08:00:00       4        0           1        2  14.76   \n",
       "6157   2012-02-12 19:00:00       1        0           0        1   6.56   \n",
       "10221  2012-11-11 07:00:00       4        0           0        1  12.30   \n",
       "1025   2011-03-07 06:00:00       1        0           1        1   8.20   \n",
       "168    2011-01-08 07:00:00       1        0           0        2   6.56   \n",
       "\n",
       "        atemp  humidity  windspeed  casual  registered  count trainortest  \n",
       "3839   28.030        88     0.0000      32         107    139        test  \n",
       "5605   20.455        37    19.9995      58         256    314        test  \n",
       "4698   20.455        76     0.0000      12         169    181        test  \n",
       "5698   21.210        77    11.0014      24         132    156        test  \n",
       "553    12.880        60    15.0013       0           1      1        test  \n",
       "...       ...       ...        ...     ...         ...    ...         ...  \n",
       "9983   18.940        56     7.0015      12         668    680        test  \n",
       "6157    6.820        40    19.0012       2          78     80        test  \n",
       "10221  15.910        89     6.0032      12          56     68        test  \n",
       "1025    9.090        75    26.0027       3          31     34        test  \n",
       "168     9.090        74     7.0015       1           8      9        test  \n",
       "\n",
       "[2722 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f2aa7c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 13)\n",
      "Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',\n",
      "       'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count',\n",
      "       'trainortest'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#合并\n",
    "all_df=pd.concat((train_df,test_df))\n",
    "print(all_df.shape)\n",
    "print(all_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7acbc805",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3236    2011-08-04\n",
      "1067    2011-03-09\n",
      "3245    2011-08-05\n",
      "2300    2011-06-03\n",
      "4716    2011-11-09\n",
      "Name: date, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#新增 日期列  date\n",
    "\n",
    "\"2011-01-01 00:00:00\"\n",
    "# x.split(' ') #\"2011-01-01,00:00:00\"\n",
    "# x.split(' ')[0] #2011-01-01\n",
    "# x 即 \"2011-01-01 00:00:00\"\n",
    "all_df['date']=all_df['datetime'].apply(lambda x:x.split(' ')[0])\n",
    "print(all_df['date'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0bd25c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3236     8\n",
      "1067     3\n",
      "3245     8\n",
      "2300     6\n",
      "4716    11\n",
      "Name: month_num, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# def lamda(x):\n",
    "#     for x in all_df['datetime']:\n",
    "#         return x.split(' ')[0]\n",
    "##2012-06-09\n",
    "\n",
    "##新增 数字 月份列\n",
    "# x.split('-') #2012,06,09\n",
    "#x.split('-')[1] '06'\n",
    "#int(x.split('-')[1]) 6\n",
    "\n",
    "all_df['month_num']=all_df['date'].apply(lambda x:int(x.split('-')[1]))\n",
    "print(all_df['month_num'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01437f38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3236      August\n",
      "1067       March\n",
      "3245      August\n",
      "2300        June\n",
      "4716    November\n",
      "Name: month, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#新增 英文 月份列\n",
    "import calendar\n",
    "all_df['month']=all_df['month_num'].apply(lambda x:calendar.month_name[x])\n",
    "print(all_df['month'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "579b4f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calendar.day_name\n",
      "<calendar._localized_day object at 0x0000018C1C81CB90>\n"
     ]
    }
   ],
   "source": [
    "#新增 英文格式 星期\n",
    "from  datetime import datetime\n",
    "print(\"calendar.day_name\")\n",
    "print(calendar.day_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f4b6271e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3236    3\n",
      "1067    2\n",
      "3245    4\n",
      "2300    4\n",
      "4716    2\n",
      "Name: weekday_num, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#1.先获取 数值型 星期\n",
    "#2012-06-09\n",
    "# dt\n",
    "#dt.weekday()\n",
    "#如何将 字符串类型的 2012-06-09，转换成 dt类型的对象\n",
    "# datetime.strptime(x,\"%Y-%m-%d\") #str字符创  parse 解析  time时间对象， 该方法的返回值 是一个日期时间类的对象\n",
    "#日期时间类的对象.weekday()\n",
    "all_df['weekday_num']=all_df['date'].apply(lambda x:datetime.strptime(x,\"%Y-%m-%d\").weekday())\n",
    "print(all_df['weekday_num'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1d2d28a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3236     Thursday\n",
      "1067    Wednesday\n",
      "3245       Friday\n",
      "2300       Friday\n",
      "4716    Wednesday\n",
      "Name: weekday, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#2.再获取 英文型 星期\n",
    "all_df['weekday']=all_df['weekday_num'].apply(lambda x:calendar.day_name[x])\n",
    "print(all_df['weekday'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ae8a91bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3236     Thursday\n",
      "1067    Wednesday\n",
      "3245       Friday\n",
      "2300       Friday\n",
      "4716    Wednesday\n",
      "Name: weekday, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#上面两个步骤 合二为一\n",
    "all_df['weekday']=all_df['date'].apply(lambda x:calendar.day_name[datetime.strptime(x,\"%Y-%m-%d\").weekday()])\n",
    "print(all_df['weekday'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "420a8c5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3236    18\n",
      "1067     0\n",
      "3245     3\n",
      "2300    18\n",
      "4716    14\n",
      "Name: hour, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#新增 小时 列\n",
    "#\"2011-01-01 10:14:00\"\n",
    "#10:14:00\n",
    "all_df['hour']=all_df['datetime'].apply(lambda x:int(x.split(' ')[1].split(':')[0]))\n",
    "print(all_df['hour'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "74f52914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s1\n",
      "month_num\n",
      "8     912\n",
      "6     912\n",
      "5     912\n",
      "12    912\n",
      "7     912\n",
      "11    911\n",
      "10    911\n",
      "9     909\n",
      "4     909\n",
      "3     901\n",
      "2     901\n",
      "1     884\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对 月份 属性列 进行频数统计\n",
    "s1=all_df['month_num'].value_counts()\n",
    "print(\"s1\")\n",
    "print(s1)\n",
    "s1.plot(kind='line')\n",
    "import matplotlib.pyplot as plt\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b4bf5c08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s2\n",
      "month_num\n",
      "1     884\n",
      "2     901\n",
      "3     901\n",
      "4     909\n",
      "5     912\n",
      "6     912\n",
      "7     912\n",
      "8     912\n",
      "9     909\n",
      "10    911\n",
      "11    911\n",
      "12    912\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用 pandas进行 某个属性 频数统计 后 折线图的绘制\n",
    "s1=all_df['month_num'].value_counts()\n",
    "s2=s1.sort_index()\n",
    "print(\"s2\")\n",
    "print(s2)\n",
    "s2.plot(kind='line')\n",
    "plt.show()\n",
    "s2.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "03ef5f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vfqczZ87ozJkzevPNN5Wbm6sFCxb4ekYAAIAWa9HTWK+//rp++9vfasSIEZ599957r0JDQzVlyhStX7/eV/MBAABclxZd2amurpbT6Wyyv2vXrjyNBQAA/EqLYmfIkCFatmyZzp8/79lXU1Ojn/3sZxoyZIjPhgMAALheLXoaKz09XePGjVO3bt3Ur18/ORwOFRYWKiQkRLt27fL1jAAAAC3WotiJj4/XZ599pi1btugvf/mLLMvS1KlTNX36dIWGhvp6RgAAgBZrUeykpaXJ6XTqxz/+sdf+l19+Wf/4xz+0ePFinwwHAABwvVr0mp1f//rXuuOOO5rs79u3r1588cXrHgoAAMBXWhQ7paWlioqKarK/S5cuKikpue6hAAAAfKVFsRMdHa0//vGPTfb/8Y9/lNvtvu6hAAAAfKVFr9l57LHHlJycrPr6eo0cOVKS9N5772nRokX8BGUAAOBXWhQ7ixYt0ldffaWkpCTV1dVJktq2bavFixdryZIlPh0QAADgerQodhwOh1544QU9//zzOnr0qEJDQxUbG6uQkBBfzwcAAHBdWhQ7F7Vv316DBg3y1SwAAAA+16IXKAMAANwsiB0AAGA0YgcAABiN2AEAAEYjdgAAgNH8JnbS0tLkcDiUnJzs2WdZllJSUuR2uxUaGqoRI0boyJEjXverra3V3Llz1blzZ4WFhWnixIk6depUK08PAAD8lV/ETn5+vrKysnTXXXd57V+9erXWrl2rzMxM5efny+VyacyYMaqqqvKsSU5OVnZ2trZt26Z9+/bp7NmzmjBhghoaGlr7NAAAgB+yPXbOnj2r6dOn66WXXlLHjh09+y3LUnp6upYuXapJkyYpLi5OmzZtUnV1tbZu3SpJqqio0IYNG7RmzRqNHj1a/fv315YtW3To0CHt3r3brlMCAAB+xPbYmTNnjsaPH6/Ro0d77S8qKlJpaakSExM9+0JCQjR8+HDl5eVJkgoKClRfX++1xu12Ky4uzrPmcmpra1VZWem1AQAAM13XT1C+Xtu2bdOBAweUn5/f5Fhpaakkyel0eu13Op06ceKEZ01wcLDXFaGLay7e/3LS0tL0s5/97HrHBwAANwHbruwUFxdr3rx52rJli9q2bdvsOofD4XXbsqwm+y51tTVLlixRRUWFZysuLr624QEAwE3DttgpKChQWVmZBgwYoMDAQAUGBio3N1e//OUvFRgY6Lmic+kVmrKyMs8xl8uluro6lZeXN7vmckJCQtShQwevDQAAmMm22Bk1apQOHTqkwsJCzzZw4EBNnz5dhYWF6tWrl1wul3Jycjz3qaurU25urhISEiRJAwYMUFBQkNeakpISHT582LMGAADc2mx7zU54eLji4uK89oWFhSkyMtKzPzk5WampqYqNjVVsbKxSU1PVrl07TZs2TZIUERGhWbNmacGCBYqMjFSnTp20cOFCxcfHN3nBMwAAuDXZ+gLlq1m0aJFqamqUlJSk8vJyDR48WLt27VJ4eLhnzbp16xQYGKgpU6aopqZGo0aN0iuvvKKAgAAbJwcAAP7Cr2Jn7969XrcdDodSUlKUkpLS7H3atm2rjIwMZWRk3NjhAADATcn2n7MDAABwIxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBotsZOWlqaBg0apPDwcHXt2lUPPPCAjh075rXGsiylpKTI7XYrNDRUI0aM0JEjR7zW1NbWau7cuercubPCwsI0ceJEnTp1qjVPBQAA+ClbYyc3N1dz5szRhx9+qJycHF24cEGJiYk6d+6cZ83q1au1du1aZWZmKj8/Xy6XS2PGjFFVVZVnTXJysrKzs7Vt2zbt27dPZ8+e1YQJE9TQ0GDHaQEAAD8SaOcXf/fdd71ub9y4UV27dlVBQYHuueceWZal9PR0LV26VJMmTZIkbdq0SU6nU1u3btXs2bNVUVGhDRs2aPPmzRo9erQkacuWLYqOjtbu3bs1duzYVj8vAADgP/zqNTsVFRWSpE6dOkmSioqKVFpaqsTERM+akJAQDR8+XHl5eZKkgoIC1dfXe61xu92Ki4vzrLlUbW2tKisrvTYAAGAmv4kdy7I0f/58DRs2THFxcZKk0tJSSZLT6fRa63Q6PcdKS0sVHBysjh07NrvmUmlpaYqIiPBs0dHRvj4dAADgJ/wmdp544gl98sknevXVV5scczgcXrcty2qy71JXWrNkyRJVVFR4tuLi4pYPDgAA/JpfxM7cuXP11ltvac+ePerWrZtnv8vlkqQmV2jKyso8V3tcLpfq6upUXl7e7JpLhYSEqEOHDl4bAAAwk62xY1mWnnjiCW3fvl3vv/++YmJivI7HxMTI5XIpJyfHs6+urk65ublKSEiQJA0YMEBBQUFea0pKSnT48GHPGgAAcOuy9d1Yc+bM0datW/Xmm28qPDzccwUnIiJCoaGhcjgcSk5OVmpqqmJjYxUbG6vU1FS1a9dO06ZN86ydNWuWFixYoMjISHXq1EkLFy5UfHy8591ZAADg1mVr7Kxfv16SNGLECK/9Gzdu1A9/+ENJ0qJFi1RTU6OkpCSVl5dr8ODB2rVrl8LDwz3r161bp8DAQE2ZMkU1NTUaNWqUXnnlFQUEBLTWqQAAAD9la+xYlnXVNQ6HQykpKUpJSWl2Tdu2bZWRkaGMjAwfTgcAAEzgFy9QBgAAuFGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGNi51e/+pViYmLUtm1bDRgwQH/4wx/sHgkAAPgBI2LntddeU3JyspYuXaqDBw/qe9/7nsaNG6eTJ0/aPRoAALCZEbGzdu1azZo1S4899pj69Omj9PR0RUdHa/369XaPBgAAbHbTx05dXZ0KCgqUmJjotT8xMVF5eXk2TQUAAPxFoN0DXK9//vOfamhokNPp9NrvdDpVWlp62fvU1taqtrbWc7uiokKSVFlZ6dlXVVNzA6a9NiH/Ns/lVJ2vb6VJmld5lRkl6VyN/89ZU3OhlSZp3tVmrKv2/xklqb76fCtMcmVXm7O+urqVJmne1Wc810qTNO+b/HnXVVe1wiRXdrU5q/1ixuArHq867w8ztrvqmqrz9v+9DP23P++Lf/aWZV35TtZN7vTp05YkKy8vz2v/ihUrrN69e1/2PsuWLbMksbGxsbGxsRmwFRcXX7EVbvorO507d1ZAQECTqzhlZWVNrvZctGTJEs2fP99zu7GxUV999ZUiIyPlcDh8MldlZaWio6NVXFysDh06+OQxfY0ZfedmmJMZfedmmJMZfedmmPNWndGyLFVVVcntdl9x3U0fO8HBwRowYIBycnL04IMPevbn5OTo/vvvv+x9QkJCFBIS4rXvW9/61g2Zr0OHDn77F+8iZvSdm2FOZvSdm2FOZvSdm2HOW3HGiIiIq6656WNHkubPn69HH31UAwcO1JAhQ5SVlaWTJ0/q8ccft3s0AABgMyNi56GHHtL//d//afny5SopKVFcXJx+//vfq0ePHnaPBgAAbGZE7EhSUlKSkpKS7B7DIyQkRMuWLWvydJk/YUbfuRnmZEbfuRnmZEbfuRnmZMYrc1jW1d6vBQAAcPO66X+oIAAAwJUQOwAAwGjEDgAAMBqx42MffPCB7rvvPrndbjkcDr3xxht2j+QlLS1NgwYNUnh4uLp27aoHHnhAx44ds3usJtavX6+77rrL8/MYhgwZoh07dtg91hWlpaXJ4XAoOTnZ7lG8pKSkyOFweG0ul8vusZo4ffq0HnnkEUVGRqpdu3a6++67VVBQYPdYHj179mzy++hwODRnzhy7R/O4cOGCnnvuOcXExCg0NFS9evXS8uXL1djYaPdoTVRVVSk5OVk9evRQaGioEhISlJ+fb9s8V/vebVmWUlJS5Ha7FRoaqhEjRujIkSN+N+f27ds1duxYde7cWQ6HQ4WFhX41Y319vRYvXqz4+HiFhYXJ7XbrBz/4gb788ssbOhOx42Pnzp1Tv379lJmZafcol5Wbm6s5c+boww8/VE5Oji5cuKDExESdO2f/5538u27dumnVqlX66KOP9NFHH2nkyJG6//77bfnm8k3k5+crKytLd911l92jXFbfvn1VUlLi2Q4dOmT3SF7Ky8s1dOhQBQUFaceOHfr000+1Zs2aG/bDPlsiPz/f6/cwJydHkjR58mSbJ/uXF154QS+++KIyMzN19OhRrV69Wj//+c+VkZFh92hNPPbYY8rJydHmzZt16NAhJSYmavTo0Tp9+rQt81zte/fq1au1du1aZWZmKj8/Xy6XS2PGjFFVVet+ptXV5jx37pyGDh2qVatWtepcl87Q3IzV1dU6cOCAnn/+eR04cEDbt2/X8ePHNXHixBs7lE8+oAqXJcnKzs62e4wrKisrsyRZubm5do9yVR07drT+93//1+4xmqiqqrJiY2OtnJwca/jw4da8efPsHsnLsmXLrH79+tk9xhUtXrzYGjZsmN1jXJN58+ZZt99+u9XY2Gj3KB7jx4+3Zs6c6bVv0qRJ1iOPPGLTRJdXXV1tBQQEWG+//bbX/n79+llLly61aap/ufR7d2Njo+VyuaxVq1Z59p0/f96KiIiwXnzxRRsm/NqV/o0pKiqyJFkHDx5s1Zku9U3+Hdy/f78lyTpx4sQNm4MrO7e4i5/43qlTJ5snaV5DQ4O2bdumc+fOaciQIXaP08ScOXM0fvx4jR492u5RmvXZZ5/J7XYrJiZGU6dO1RdffGH3SF7eeustDRw4UJMnT1bXrl3Vv39/vfTSS3aP1ay6ujpt2bJFM2fO9Nnn6fnCsGHD9N577+n48eOSpI8//lj79u3Tvffea/Nk3i5cuKCGhga1bdvWa39oaKj27dtn01TNKyoqUmlpqRITEz37QkJCNHz4cOXl5dk4mRkqKirkcDhu6JVcY36oIK6dZVmaP3++hg0bpri4OLvHaeLQoUMaMmSIzp8/r/bt2ys7O1t33nmn3WN52bZtmw4cOGDraw2uZvDgwfrNb36j73znO/r73/+uFStWKCEhQUeOHFFkZKTd40mSvvjiC61fv17z58/Xs88+q/379+vJJ59USEiIfvCDH9g9XhNvvPGGzpw5ox/+8Id2j+Jl8eLFqqio0B133KGAgAA1NDRo5cqVevjhh+0ezUt4eLiGDBmi//qv/1KfPn3kdDr16quv6s9//rNiY2PtHq+Jix80femHSzudTp04ccKOkYxx/vx5PfPMM5o2bdoN/UwvYucW9sQTT+iTTz7xy/9JSVLv3r1VWFioM2fO6PXXX9eMGTOUm5vrN8FTXFysefPmadeuXU3+h+pPxo0b5/l1fHy8hgwZottvv12bNm3S/PnzbZzsXxobGzVw4EClpqZKkvr3768jR45o/fr1fhk7GzZs0Lhx4676Scut7bXXXtOWLVu0detW9e3bV4WFhUpOTpbb7daMGTPsHs/L5s2bNXPmTN12220KCAjQd7/7XU2bNk0HDhywe7RmXXoVz7Isv7qyd7Opr6/X1KlT1djYqF/96lc39GsRO7eouXPn6q233tIHH3ygbt262T3OZQUHB+vb3/62JGngwIHKz8/XL37xC/3617+2ebKvFRQUqKysTAMGDPDsa2ho0AcffKDMzEzV1tYqICDAxgkvLywsTPHx8frss8/sHsUjKiqqScT26dNHr7/+uk0TNe/EiRPavXu3tm/fbvcoTTz99NN65plnNHXqVElfx+2JEyeUlpbmd7Fz++23Kzc3V+fOnVNlZaWioqL00EMPKSYmxu7Rmrj47sXS0lJFRUV59peVlTW52oNvpr6+XlOmTFFRUZHef//9G/5J7bxm5xZjWZaeeOIJbd++Xe+//75ffmNpjmVZqq2ttXsMj1GjRunQoUMqLCz0bAMHDtT06dNVWFjol6EjSbW1tTp69KjXN227DR06tMmPQDh+/Lhffpjvxo0b1bVrV40fP97uUZqorq5Wmzbe39YDAgL88q3nF4WFhSkqKkrl5eXauXOn7r//frtHaiImJkYul8vzDjzp69dt5ebmKiEhwcbJbk4XQ+ezzz7T7t27W+XpdK7s+NjZs2f117/+1XO7qKhIhYWF6tSpk7p3727jZF+bM2eOtm7dqjfffFPh4eGe56IjIiIUGhpq83T/8uyzz2rcuHGKjo5WVVWVtm3bpr179+rdd9+1ezSP8PDwJq91CgsLU2RkpF+9BmrhwoW677771L17d5WVlWnFihWqrKz0q//pP/XUU0pISFBqaqqmTJmi/fv3KysrS1lZWXaP5qWxsVEbN27UjBkzFBjof98+77vvPq1cuVLdu3dX3759dfDgQa1du1YzZ860e7Qmdu7cKcuy1Lt3b/31r3/V008/rd69e+tHP/qRLfNc7Xt3cnKyUlNTFRsbq9jYWKWmpqpdu3aaNm2aX8351Vdf6eTJk56fW3PxPxEul6vVfr7WlWZ0u936/ve/rwMHDujtt99WQ0OD59+hTp06KTg4+MYMdcPe53WL2rNnjyWpyTZjxgy7R7Msy7rsbJKsjRs32j2al5kzZ1o9evSwgoODrS5dulijRo2ydu3aZfdYV+WPbz1/6KGHrKioKCsoKMhyu93WpEmTrCNHjtg9VhO/+93vrLi4OCskJMS64447rKysLLtHamLnzp2WJOvYsWN2j3JZlZWV1rx586zu3btbbdu2tXr16mUtXbrUqq2ttXu0Jl577TWrV69eVnBwsOVyuaw5c+ZYZ86csW2eq33vbmxstJYtW2a5XC4rJCTEuueee6xDhw753ZwbN2687PFly5b5xYwX3xJ/uW3Pnj03bCY+9RwAABiN1+wAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsADBKSkqK7r77brvHAOBHiB0ANy2Hw6E33njD7jEA+DliBwAAGI3YAXDdRowYoblz5yo5OVkdO3aU0+lUVlaWzp07px/96EcKDw/X7bffrh07dnjuk5ubq//4j/9QSEiIoqKi9Mwzz+jChQtej/nkk09q0aJF6tSpk1wul1JSUjzHe/bsKUl68MEH5XA4PLcv2rx5s3r27KmIiAhNnTpVVVVV3/hcrvR1//a3v8nhcKiwsNCz78yZM3I4HNq7d68kae/evXI4HNq5c6f69++v0NBQjRw5UmVlZdqxY4f69OmjDh066OGHH1Z1dfU3mgtAyxE7AHxi06ZN6ty5s/bv36+5c+fqJz/5iSZPnqyEhAQdOHBAY8eO1aOPPqrq6mqdPn1a9957rwYNGqSPP/5Y69ev14YNG7RixYomjxkWFqY///nPWr16tZYvX66cnBxJUn5+viRp48aNKikp8dyWpM8//1xvvPGG3n77bb399tvKzc3VqlWrrulcmvu61yIlJUWZmZnKy8tTcXGxpkyZovT0dG3dulXvvPOOcnJylJGRcc2PC+Aa3bDPUwdwyxg+fLg1bNgwz+0LFy5YYWFh1qOPPurZV1JSYkmy/vSnP1nPPvus1bt3b6uxsdFz/H/+53+s9u3bWw0NDZd9TMuyrEGDBlmLFy/23JZkZWdne61ZtmyZ1a5dO6uystKz7+mnn7YGDx7conO59OsWFRVZkqyDBw96jpeXl1uSrD179liWZVl79uyxJFm7d+/2rElLS7MkWZ9//rln3+zZs62xY8d+o7kAtBxXdgD4xF133eX5dUBAgCIjIxUfH+/Z53Q6JUllZWU6evSohgwZIofD4Tk+dOhQnT17VqdOnbrsY0pSVFSUysrKrjpLz549FR4efs33u96ve6XHcTqdateunXr16uW1ryWPC+DaEDsAfCIoKMjrtsPh8Np3MWwaGxtlWZZX6EiSZVle65p7zMbGxhbN8k3u903u36ZNG695Jam+vv6qj3Pp70dL5gLQMsQOgFZ35513Ki8vzysY8vLyFB4erttuu+0bP05QUJAaGhpuxIjN6tKliySppKTEs+/fX6wMwP8QOwBaXVJSkoqLizV37lz95S9/0Ztvvqlly5Zp/vz5nisn30TPnj313nvvqbS0VOXl5Tdw4n8JDQ3Vf/7nf2rVqlX69NNP9cEHH+i5555rla8NoGWIHQCt7rbbbtPvf/977d+/X/369dPjjz+uWbNmXXM0rFmzRjk5OYqOjlb//v1v0LRNvfzyy6qvr9fAgQM1b968Ju8iA+BfHNa/X0cGAAAwDFd2AACA0YgdALeMkydPqn379s1uJ0+etHtEADcAT2MBuGVcuHBBf/vb35o93rNnTwUGBrbeQABaBbEDAACMxtNYAADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKP9/76VPcTO9K9aAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用 seaborn 进行 某个属性 频数统计后 柱状图 的绘制\n",
    "import seaborn as sns\n",
    "sns.countplot(data=all_df,x='month_num')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "af9567e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#统计 不同小时，用车的均值\n",
    "#1.根据小时 进行分组\n",
    "g = all_df.groupby('hour')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9605fc37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>month_num</th>\n",
       "      <th>weekday_num</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.503297</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.681319</td>\n",
       "      <td>1.393407</td>\n",
       "      <td>19.013187</td>\n",
       "      <td>22.462582</td>\n",
       "      <td>68.079121</td>\n",
       "      <td>10.701564</td>\n",
       "      <td>10.312088</td>\n",
       "      <td>44.826374</td>\n",
       "      <td>55.138462</td>\n",
       "      <td>6.512088</td>\n",
       "      <td>3.013187</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.502203</td>\n",
       "      <td>0.028634</td>\n",
       "      <td>0.680617</td>\n",
       "      <td>1.431718</td>\n",
       "      <td>18.639648</td>\n",
       "      <td>22.011476</td>\n",
       "      <td>69.581498</td>\n",
       "      <td>10.418839</td>\n",
       "      <td>6.513216</td>\n",
       "      <td>27.345815</td>\n",
       "      <td>33.859031</td>\n",
       "      <td>6.506608</td>\n",
       "      <td>3.017621</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.522321</td>\n",
       "      <td>0.029018</td>\n",
       "      <td>0.680804</td>\n",
       "      <td>1.401786</td>\n",
       "      <td>18.455491</td>\n",
       "      <td>21.822623</td>\n",
       "      <td>70.622768</td>\n",
       "      <td>10.125315</td>\n",
       "      <td>4.819196</td>\n",
       "      <td>18.080357</td>\n",
       "      <td>22.899554</td>\n",
       "      <td>6.560268</td>\n",
       "      <td>3.017857</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.556582</td>\n",
       "      <td>0.027714</td>\n",
       "      <td>0.667436</td>\n",
       "      <td>1.401848</td>\n",
       "      <td>18.433903</td>\n",
       "      <td>21.814007</td>\n",
       "      <td>72.293303</td>\n",
       "      <td>10.173416</td>\n",
       "      <td>2.681293</td>\n",
       "      <td>9.076212</td>\n",
       "      <td>11.757506</td>\n",
       "      <td>6.697460</td>\n",
       "      <td>3.087760</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.545249</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.671946</td>\n",
       "      <td>1.427602</td>\n",
       "      <td>18.036290</td>\n",
       "      <td>21.352738</td>\n",
       "      <td>73.640271</td>\n",
       "      <td>10.717605</td>\n",
       "      <td>1.262443</td>\n",
       "      <td>5.144796</td>\n",
       "      <td>6.407240</td>\n",
       "      <td>6.638009</td>\n",
       "      <td>3.024887</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.513274</td>\n",
       "      <td>0.028761</td>\n",
       "      <td>0.685841</td>\n",
       "      <td>1.422566</td>\n",
       "      <td>17.610044</td>\n",
       "      <td>20.882002</td>\n",
       "      <td>73.409292</td>\n",
       "      <td>10.062407</td>\n",
       "      <td>1.455752</td>\n",
       "      <td>18.311947</td>\n",
       "      <td>19.767699</td>\n",
       "      <td>6.542035</td>\n",
       "      <td>2.993363</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.503297</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.681319</td>\n",
       "      <td>1.450549</td>\n",
       "      <td>17.481319</td>\n",
       "      <td>20.722747</td>\n",
       "      <td>73.934066</td>\n",
       "      <td>10.433402</td>\n",
       "      <td>4.149451</td>\n",
       "      <td>72.109890</td>\n",
       "      <td>76.259341</td>\n",
       "      <td>6.512088</td>\n",
       "      <td>3.013187</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.503297</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.681319</td>\n",
       "      <td>1.479121</td>\n",
       "      <td>17.787692</td>\n",
       "      <td>21.035659</td>\n",
       "      <td>72.292308</td>\n",
       "      <td>10.879283</td>\n",
       "      <td>10.914286</td>\n",
       "      <td>202.202198</td>\n",
       "      <td>213.116484</td>\n",
       "      <td>6.512088</td>\n",
       "      <td>3.013187</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.503297</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.681319</td>\n",
       "      <td>1.465934</td>\n",
       "      <td>18.461714</td>\n",
       "      <td>21.801637</td>\n",
       "      <td>69.553846</td>\n",
       "      <td>11.787155</td>\n",
       "      <td>21.542857</td>\n",
       "      <td>341.226374</td>\n",
       "      <td>362.769231</td>\n",
       "      <td>6.512088</td>\n",
       "      <td>3.013187</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.503297</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.681319</td>\n",
       "      <td>1.474725</td>\n",
       "      <td>19.359209</td>\n",
       "      <td>22.752319</td>\n",
       "      <td>65.459341</td>\n",
       "      <td>12.929984</td>\n",
       "      <td>30.956044</td>\n",
       "      <td>190.824176</td>\n",
       "      <td>221.780220</td>\n",
       "      <td>6.512088</td>\n",
       "      <td>3.013187</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.503297</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.681319</td>\n",
       "      <td>1.454945</td>\n",
       "      <td>20.319780</td>\n",
       "      <td>23.696286</td>\n",
       "      <td>60.850549</td>\n",
       "      <td>13.652980</td>\n",
       "      <td>46.118681</td>\n",
       "      <td>128.973626</td>\n",
       "      <td>175.092308</td>\n",
       "      <td>6.512088</td>\n",
       "      <td>3.013187</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.503297</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.681319</td>\n",
       "      <td>1.432967</td>\n",
       "      <td>21.262330</td>\n",
       "      <td>24.710231</td>\n",
       "      <td>56.413187</td>\n",
       "      <td>14.039931</td>\n",
       "      <td>60.052747</td>\n",
       "      <td>150.621978</td>\n",
       "      <td>210.674725</td>\n",
       "      <td>6.512088</td>\n",
       "      <td>3.013187</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.438596</td>\n",
       "      <td>22.001535</td>\n",
       "      <td>25.501732</td>\n",
       "      <td>52.892544</td>\n",
       "      <td>14.566109</td>\n",
       "      <td>68.831140</td>\n",
       "      <td>187.677632</td>\n",
       "      <td>256.508772</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.418860</td>\n",
       "      <td>22.690263</td>\n",
       "      <td>26.249232</td>\n",
       "      <td>50.065789</td>\n",
       "      <td>15.195453</td>\n",
       "      <td>74.059211</td>\n",
       "      <td>183.728070</td>\n",
       "      <td>257.787281</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>23.147018</td>\n",
       "      <td>26.714452</td>\n",
       "      <td>48.379386</td>\n",
       "      <td>15.769978</td>\n",
       "      <td>76.589912</td>\n",
       "      <td>166.853070</td>\n",
       "      <td>243.442982</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.414474</td>\n",
       "      <td>23.281886</td>\n",
       "      <td>26.832346</td>\n",
       "      <td>48.076754</td>\n",
       "      <td>15.894839</td>\n",
       "      <td>76.028509</td>\n",
       "      <td>178.269737</td>\n",
       "      <td>254.298246</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.381579</td>\n",
       "      <td>23.118246</td>\n",
       "      <td>26.621382</td>\n",
       "      <td>48.508772</td>\n",
       "      <td>16.133997</td>\n",
       "      <td>75.083333</td>\n",
       "      <td>241.289474</td>\n",
       "      <td>316.372807</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.412281</td>\n",
       "      <td>22.654298</td>\n",
       "      <td>26.089825</td>\n",
       "      <td>50.421053</td>\n",
       "      <td>15.840034</td>\n",
       "      <td>75.440789</td>\n",
       "      <td>393.324561</td>\n",
       "      <td>468.765351</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.423246</td>\n",
       "      <td>22.107632</td>\n",
       "      <td>25.564748</td>\n",
       "      <td>52.649123</td>\n",
       "      <td>15.283126</td>\n",
       "      <td>61.396930</td>\n",
       "      <td>369.462719</td>\n",
       "      <td>430.859649</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.368421</td>\n",
       "      <td>21.364956</td>\n",
       "      <td>24.837259</td>\n",
       "      <td>56.432018</td>\n",
       "      <td>14.234899</td>\n",
       "      <td>49.074561</td>\n",
       "      <td>266.203947</td>\n",
       "      <td>315.278509</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.364035</td>\n",
       "      <td>20.749956</td>\n",
       "      <td>24.272357</td>\n",
       "      <td>59.342105</td>\n",
       "      <td>13.096634</td>\n",
       "      <td>36.732456</td>\n",
       "      <td>191.785088</td>\n",
       "      <td>228.517544</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.377193</td>\n",
       "      <td>20.217675</td>\n",
       "      <td>23.720789</td>\n",
       "      <td>62.407895</td>\n",
       "      <td>12.037446</td>\n",
       "      <td>28.567982</td>\n",
       "      <td>144.802632</td>\n",
       "      <td>173.370614</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>19.766316</td>\n",
       "      <td>23.232379</td>\n",
       "      <td>64.567982</td>\n",
       "      <td>11.844718</td>\n",
       "      <td>22.603070</td>\n",
       "      <td>110.973684</td>\n",
       "      <td>133.576754</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.028509</td>\n",
       "      <td>0.682018</td>\n",
       "      <td>1.414474</td>\n",
       "      <td>19.343728</td>\n",
       "      <td>22.775548</td>\n",
       "      <td>66.649123</td>\n",
       "      <td>11.077304</td>\n",
       "      <td>15.462719</td>\n",
       "      <td>74.046053</td>\n",
       "      <td>89.508772</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.008772</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        season   holiday  workingday   weather       temp      atemp  \\\n",
       "hour                                                                   \n",
       "0     2.503297  0.028571    0.681319  1.393407  19.013187  22.462582   \n",
       "1     2.502203  0.028634    0.680617  1.431718  18.639648  22.011476   \n",
       "2     2.522321  0.029018    0.680804  1.401786  18.455491  21.822623   \n",
       "3     2.556582  0.027714    0.667436  1.401848  18.433903  21.814007   \n",
       "4     2.545249  0.029412    0.671946  1.427602  18.036290  21.352738   \n",
       "5     2.513274  0.028761    0.685841  1.422566  17.610044  20.882002   \n",
       "6     2.503297  0.028571    0.681319  1.450549  17.481319  20.722747   \n",
       "7     2.503297  0.028571    0.681319  1.479121  17.787692  21.035659   \n",
       "8     2.503297  0.028571    0.681319  1.465934  18.461714  21.801637   \n",
       "9     2.503297  0.028571    0.681319  1.474725  19.359209  22.752319   \n",
       "10    2.503297  0.028571    0.681319  1.454945  20.319780  23.696286   \n",
       "11    2.503297  0.028571    0.681319  1.432967  21.262330  24.710231   \n",
       "12    2.500000  0.028509    0.682018  1.438596  22.001535  25.501732   \n",
       "13    2.500000  0.028509    0.682018  1.418860  22.690263  26.249232   \n",
       "14    2.500000  0.028509    0.682018  1.416667  23.147018  26.714452   \n",
       "15    2.500000  0.028509    0.682018  1.414474  23.281886  26.832346   \n",
       "16    2.500000  0.028509    0.682018  1.381579  23.118246  26.621382   \n",
       "17    2.500000  0.028509    0.682018  1.412281  22.654298  26.089825   \n",
       "18    2.500000  0.028509    0.682018  1.423246  22.107632  25.564748   \n",
       "19    2.500000  0.028509    0.682018  1.368421  21.364956  24.837259   \n",
       "20    2.500000  0.028509    0.682018  1.364035  20.749956  24.272357   \n",
       "21    2.500000  0.028509    0.682018  1.377193  20.217675  23.720789   \n",
       "22    2.500000  0.028509    0.682018  1.375000  19.766316  23.232379   \n",
       "23    2.500000  0.028509    0.682018  1.414474  19.343728  22.775548   \n",
       "\n",
       "       humidity  windspeed     casual  registered       count  month_num  \\\n",
       "hour                                                                       \n",
       "0     68.079121  10.701564  10.312088   44.826374   55.138462   6.512088   \n",
       "1     69.581498  10.418839   6.513216   27.345815   33.859031   6.506608   \n",
       "2     70.622768  10.125315   4.819196   18.080357   22.899554   6.560268   \n",
       "3     72.293303  10.173416   2.681293    9.076212   11.757506   6.697460   \n",
       "4     73.640271  10.717605   1.262443    5.144796    6.407240   6.638009   \n",
       "5     73.409292  10.062407   1.455752   18.311947   19.767699   6.542035   \n",
       "6     73.934066  10.433402   4.149451   72.109890   76.259341   6.512088   \n",
       "7     72.292308  10.879283  10.914286  202.202198  213.116484   6.512088   \n",
       "8     69.553846  11.787155  21.542857  341.226374  362.769231   6.512088   \n",
       "9     65.459341  12.929984  30.956044  190.824176  221.780220   6.512088   \n",
       "10    60.850549  13.652980  46.118681  128.973626  175.092308   6.512088   \n",
       "11    56.413187  14.039931  60.052747  150.621978  210.674725   6.512088   \n",
       "12    52.892544  14.566109  68.831140  187.677632  256.508772   6.500000   \n",
       "13    50.065789  15.195453  74.059211  183.728070  257.787281   6.500000   \n",
       "14    48.379386  15.769978  76.589912  166.853070  243.442982   6.500000   \n",
       "15    48.076754  15.894839  76.028509  178.269737  254.298246   6.500000   \n",
       "16    48.508772  16.133997  75.083333  241.289474  316.372807   6.500000   \n",
       "17    50.421053  15.840034  75.440789  393.324561  468.765351   6.500000   \n",
       "18    52.649123  15.283126  61.396930  369.462719  430.859649   6.500000   \n",
       "19    56.432018  14.234899  49.074561  266.203947  315.278509   6.500000   \n",
       "20    59.342105  13.096634  36.732456  191.785088  228.517544   6.500000   \n",
       "21    62.407895  12.037446  28.567982  144.802632  173.370614   6.500000   \n",
       "22    64.567982  11.844718  22.603070  110.973684  133.576754   6.500000   \n",
       "23    66.649123  11.077304  15.462719   74.046053   89.508772   6.500000   \n",
       "\n",
       "      weekday_num  hour  \n",
       "hour                     \n",
       "0        3.013187   0.0  \n",
       "1        3.017621   1.0  \n",
       "2        3.017857   2.0  \n",
       "3        3.087760   3.0  \n",
       "4        3.024887   4.0  \n",
       "5        2.993363   5.0  \n",
       "6        3.013187   6.0  \n",
       "7        3.013187   7.0  \n",
       "8        3.013187   8.0  \n",
       "9        3.013187   9.0  \n",
       "10       3.013187  10.0  \n",
       "11       3.013187  11.0  \n",
       "12       3.008772  12.0  \n",
       "13       3.008772  13.0  \n",
       "14       3.008772  14.0  \n",
       "15       3.008772  15.0  \n",
       "16       3.008772  16.0  \n",
       "17       3.008772  17.0  \n",
       "18       3.008772  18.0  \n",
       "19       3.008772  19.0  \n",
       "20       3.008772  20.0  \n",
       "21       3.008772  21.0  \n",
       "22       3.008772  22.0  \n",
       "23       3.008772  23.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.使用分组对象的 聚合函数 进行 组内求和，得到的是 数据帧(组内 所有 属性求和)\n",
    "import numpy as np\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "df2 = g.apply(mean_numeric_columns)  # 对每个分组应用函数计算均值\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "36b06eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3.取出 求和 数据帧中的 count求和列，作为一个系列series\n",
    "s3=df2['count']\n",
    "#4.绘制折线图\n",
    "s3.plot(kind='line')\n",
    "import matplotlib.pyplot as plt\n",
    "plt.show()\n",
    "# sns.barplot(data=all_df,x='hour',y='count')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "45aaa357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_df['hour_section'].head()\n",
      "3236    3\n",
      "1067    0\n",
      "3245    0\n",
      "2300    3\n",
      "4716    2\n",
      "Name: hour_section, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#根据上图 将 hour 可以分为 几个 时间段 hour_section\n",
    "def f(x):\n",
    "    if x>=0 and x<=6:\n",
    "        return 0\n",
    "    elif x>=7 and x<=10:\n",
    "        return 1\n",
    "    elif x>=11 and x<=15:\n",
    "        return 2\n",
    "    elif x>=16 and x<=20:\n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "all_df['hour_section']= all_df['hour'].apply(f)\n",
    "print(\"all_df['hour_section'].head()\")\n",
    "print(all_df['hour_section'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "12ef65be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#三、数据降噪\n",
    "#1.四个箱线图\n",
    "import matplotlib.pyplot as plt\n",
    "fig,axes=plt.subplots(nrows=2,ncols=2)#fig画布对象 #axes四张图的坐标\n",
    "fig.set_size_inches(12,10) #设置 画图对象大小\n",
    "#（1）所有数据 ，用车数量count的箱线图\n",
    "import seaborn as sns\n",
    "sns.boxplot(data=all_df,y='count',ax=axes[0][0])#\n",
    "#（2）对 季节（season）分组,绘制 不同季节的 用车数量(count) 箱线图\n",
    "sns.boxplot(data=all_df,x='season',y='count',ax=axes[0][1])#\n",
    "#(3) 对 小时（hour）分组，绘制 不同 小时 的 用车数量(count) 箱线图\n",
    "sns.boxplot(data=all_df,x='hour',y='count',ax=axes[1][0])#\n",
    "#(4)对  是否是工作日（workingday）分组，绘制 是否是工作日 的 用车数量(count) 箱线图\n",
    "sns.boxplot(data=all_df,x='workingday',y='count',ax=axes[1][1])#\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e705183",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3236    True\n",
      "1067    True\n",
      "3245    True\n",
      "2300    True\n",
      "4716    True\n",
      "Name: count, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "#删除噪音点：\n",
    "# 删除依据 (某属性值-某属性均值)>3*该属性标准差     是 噪音\n",
    "#1.根据 用车数量 count 进行筛选，获取 非噪音点的行索引\n",
    "# 1√\n",
    "#2 ×\n",
    "#3 √\n",
    "#保留 正常数据(非噪音数据 依据) (某属性值-某属性均值) < 3*该属性标准差\n",
    "ix = (all_df['count']-all_df['count'].mean()) < 3*all_df['count'].std()\n",
    "print(ix.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89be46cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "筛选前数据帧的shape (10886, 20)\n",
      "筛选后数据帧的shape (10739, 20)\n"
     ]
    }
   ],
   "source": [
    "#2.根据 非噪音数据 的索引 获取 数据\n",
    "print(\"筛选前数据帧的shape\",all_df.shape)\n",
    "all_df=all_df[ix]\n",
    "print(\"筛选后数据帧的shape\",all_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1a64974c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3236</th>\n",
       "      <td>28.70</td>\n",
       "      <td>33.335</td>\n",
       "      <td>88</td>\n",
       "      <td>435</td>\n",
       "      <td>74</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>10.66</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3245</th>\n",
       "      <td>26.24</td>\n",
       "      <td>29.545</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>28.70</td>\n",
       "      <td>31.820</td>\n",
       "      <td>76</td>\n",
       "      <td>488</td>\n",
       "      <td>24</td>\n",
       "      <td>19.9995</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4716</th>\n",
       "      <td>21.32</td>\n",
       "      <td>25.000</td>\n",
       "      <td>26</td>\n",
       "      <td>153</td>\n",
       "      <td>48</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>14.76</td>\n",
       "      <td>18.940</td>\n",
       "      <td>12</td>\n",
       "      <td>668</td>\n",
       "      <td>56</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6157</th>\n",
       "      <td>6.56</td>\n",
       "      <td>6.820</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>40</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10221</th>\n",
       "      <td>12.30</td>\n",
       "      <td>15.910</td>\n",
       "      <td>12</td>\n",
       "      <td>56</td>\n",
       "      <td>89</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>8.20</td>\n",
       "      <td>9.090</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>75</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>6.56</td>\n",
       "      <td>9.090</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>7.0015</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10739 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        temp   atemp  casual  registered  humidity  windspeed  count\n",
       "3236   28.70  33.335      88         435        74    19.9995    523\n",
       "1067   10.66  14.395       0           9        65     6.0032      9\n",
       "3245   26.24  29.545       0           6        78     6.0032      6\n",
       "2300   28.70  31.820      76         488        24    19.9995    564\n",
       "4716   21.32  25.000      26         153        48    11.0014    179\n",
       "...      ...     ...     ...         ...       ...        ...    ...\n",
       "9983   14.76  18.940      12         668        56     7.0015    680\n",
       "6157    6.56   6.820       2          78        40    19.0012     80\n",
       "10221  12.30  15.910      12          56        89     6.0032     68\n",
       "1025    8.20   9.090       3          31        75    26.0027     34\n",
       "168     6.56   9.090       1           8        74     7.0015      9\n",
       "\n",
       "[10739 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#四、数据分析\n",
    "#(0)先获取 部分属性 [\"temp\",\"atemp\",\"casual\",\"registered\",\"humidity\",\"windspeed\",\"count\"]\n",
    "df2=all_df[[\"temp\",\"atemp\",\"casual\",\"registered\",\"humidity\",\"windspeed\",\"count\"]]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c153dd91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985254</td>\n",
       "      <td>0.460774</td>\n",
       "      <td>0.304328</td>\n",
       "      <td>-0.056394</td>\n",
       "      <td>-0.019460</td>\n",
       "      <td>0.385954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.985254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.456319</td>\n",
       "      <td>0.301074</td>\n",
       "      <td>-0.035467</td>\n",
       "      <td>-0.059403</td>\n",
       "      <td>0.381967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0.460774</td>\n",
       "      <td>0.456319</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498085</td>\n",
       "      <td>-0.341204</td>\n",
       "      <td>0.092334</td>\n",
       "      <td>0.704764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>0.304328</td>\n",
       "      <td>0.301074</td>\n",
       "      <td>0.498085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.263525</td>\n",
       "      <td>0.096104</td>\n",
       "      <td>0.966209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>-0.056394</td>\n",
       "      <td>-0.035467</td>\n",
       "      <td>-0.341204</td>\n",
       "      <td>-0.263525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.320158</td>\n",
       "      <td>-0.317028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>-0.019460</td>\n",
       "      <td>-0.059403</td>\n",
       "      <td>0.092334</td>\n",
       "      <td>0.096104</td>\n",
       "      <td>-0.320158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.106074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.385954</td>\n",
       "      <td>0.381967</td>\n",
       "      <td>0.704764</td>\n",
       "      <td>0.966209</td>\n",
       "      <td>-0.317028</td>\n",
       "      <td>0.106074</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                temp     atemp    casual  registered  humidity  windspeed  \\\n",
       "temp        1.000000  0.985254  0.460774    0.304328 -0.056394  -0.019460   \n",
       "atemp       0.985254  1.000000  0.456319    0.301074 -0.035467  -0.059403   \n",
       "casual      0.460774  0.456319  1.000000    0.498085 -0.341204   0.092334   \n",
       "registered  0.304328  0.301074  0.498085    1.000000 -0.263525   0.096104   \n",
       "humidity   -0.056394 -0.035467 -0.341204   -0.263525  1.000000  -0.320158   \n",
       "windspeed  -0.019460 -0.059403  0.092334    0.096104 -0.320158   1.000000   \n",
       "count       0.385954  0.381967  0.704764    0.966209 -0.317028   0.106074   \n",
       "\n",
       "               count  \n",
       "temp        0.385954  \n",
       "atemp       0.381967  \n",
       "casual      0.704764  \n",
       "registered  0.966209  \n",
       "humidity   -0.317028  \n",
       "windspeed   0.106074  \n",
       "count       1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #(0) 计算 以上属性的 相关系数\n",
    "df3 = df2.corr() #获得 相关系数矩阵(数据帧类型对象)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e26ac8b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.98525358,  0.46077362,  0.30432822, -0.05639378,\n",
       "        -0.0194597 ,  0.38595372],\n",
       "       [ 0.98525358,  1.        ,  0.45631899,  0.30107447, -0.03546652,\n",
       "        -0.05940275,  0.3819675 ],\n",
       "       [ 0.46077362,  0.45631899,  1.        ,  0.49808542, -0.34120427,\n",
       "         0.09233365,  0.70476411],\n",
       "       [ 0.30432822,  0.30107447,  0.49808542,  1.        , -0.26352526,\n",
       "         0.09610353,  0.96620948],\n",
       "       [-0.05639378, -0.03546652, -0.34120427, -0.26352526,  1.        ,\n",
       "        -0.32015832, -0.31702815],\n",
       "       [-0.0194597 , -0.05940275,  0.09233365,  0.09610353, -0.32015832,\n",
       "         1.        ,  0.10607385],\n",
       "       [ 0.38595372,  0.3819675 ,  0.70476411,  0.96620948, -0.31702815,\n",
       "         0.10607385,  1.        ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #一。绘制 下三角 热力图1\n",
    "# #(1)获取 同型矩阵\n",
    "import  numpy as np\n",
    "mask =np.array(df3)\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d692d338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6,\n",
       "        6, 6, 6, 6, 6, 6]),\n",
       " array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 5, 0,\n",
       "        1, 2, 3, 4, 5, 6]))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # #(2)先获取 下三角 区域 的坐标（横纵坐标）\n",
    "ix = np.tril_indices_from(mask)\n",
    "ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "709ea75f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # （3）对 mask中 下三角区域 赋值为False，其余部分 由于 存在 数值，因此会当做True\n",
    "mask[ix]=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b2b06d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import  seaborn as sns\n",
    "# #（3）热力图 中 ，mask 中 是True的位置 会被覆盖掉 （指向覆盖掉 上三角，保留下三角）\n",
    "sns.heatmap(data=df3,mask=mask,annot=True)\n",
    "import  matplotlib.pyplot as plt\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cf7d6bcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s2\n",
      "season\n",
      "1    115.369128\n",
      "2    206.779510\n",
      "3    219.048048\n",
      "4    190.904039\n",
      "Name: count, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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OQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOL0eKC0t7enUql0W1paWmr7q9Vq2tvb09ramsMPPzyjR4/Oiy++2NPTAAD2Y/vkDMqHP/zhbNiwoba88MILtX2zZs3K7NmzM2/evKxcuTItLS0ZO3ZstmzZsi+mAgDsh3rvkyft3bvbWZPfqFarmTNnTqZPn55JkyYlSR588ME0Nzdn4cKFue6663b7fF1dXenq6qqtd3Z27otpAwCF2CdnUF5++eW0trZm6NChufTSS/Pqq68mSdauXZuOjo6MGzeuNrahoSFnnXVWli9f/q7PN3PmzDQ1NdWWtra2fTFtAKAQPR4op512Wr7yla/ksccey3333ZeOjo6MGjUqb775Zjo6OpIkzc3N3R7T3Nxc27c706ZNy+bNm2vLunXrenraAEBBevwjnvHjx9f+PWLEiJxxxhn5oz/6ozz44IM5/fTTkySVSqXbY6rV6i7b/r+GhoY0NDT09FQBgELt868Z9+vXLyNGjMjLL79cuy7lnWdLNm7cuMtZFQDg4LXPA6WrqysvvfRSBg8enKFDh6alpSVLliyp7d++fXuWLl2aUaNG7eupAAD7iR7/iOeWW27JhRdemKOPPjobN27MXXfdlc7Ozlx11VWpVCqZPHlyZsyYkWHDhmXYsGGZMWNG+vbtm8svv7ynpwIA7Kd6PFDWr1+fyy67LD//+c/z+7//+zn99NOzYsWKDBkyJEly2223Zdu2bbnhhhuyadOmnHbaaXn88cfT2NjY01MBAPZTPR4oixYtes/9lUol7e3taW9v7+mXBgAOEO7FAwAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABSnroHypS99KUOHDs1hhx2WkSNH5oc//GE9pwMAFKJugfK1r30tkydPzvTp0/Pss8/mYx/7WMaPH5/XX3+9XlMCAApRt0CZPXt2rrnmmnzqU5/KCSeckDlz5qStrS3z58+v15QAgEL0rseLbt++PatXr85nPvOZbtvHjRuX5cuX7zK+q6srXV1dtfXNmzcnSTo7O/ftRN/Fzq5f1uV1661eP+96c7wPLo73wcXxrs/rVqvV9x1bl0D5+c9/nh07dqS5ubnb9ubm5nR0dOwyfubMmfn85z+/y/a2trZ9Nkd21TSn3jPgd8nxPrg43geXeh/vLVu2pKmp6T3H1CVQfqNSqXRbr1aru2xLkmnTpmXKlCm19Z07d+Z//ud/MnDgwN2OP1B1dnamra0t69atS//+/es9HfYxx/vg4ngfXA7W412tVrNly5a0tra+79i6BMqgQYPSq1evXc6WbNy4cZezKknS0NCQhoaGbtt+7/d+b19OsWj9+/c/qP5DH+wc74OL431wORiP9/udOfmNulwke+ihh2bkyJFZsmRJt+1LlizJqFGj6jElAKAgdfuIZ8qUKfnLv/zLnHLKKTnjjDPy5S9/Oa+//nr++q//ul5TAgAKUbdAueSSS/Lmm2/mC1/4QjZs2JDhw4fnu9/9boYMGVKvKRWvoaEhd9xxxy4fd3FgcrwPLo73wcXxfn+V6m/zXR8AgN8h9+IBAIojUACA4ggUAKA4AgUAKI5AAYA68B2V9yZQAKAOGhoa8tJLL9V7GsWq6714+GDWrVuXO+64I//0T/9U76nQA7Zt25bVq1dnwIABOfHEE7vt+9///d98/etfz5VXXlmn2dHTXnrppaxYsSJnnHFGjj/++PzHf/xH/v7v/z5dXV254oorcs4559R7ivSQ/38vuf9vx44dufvuuzNw4MAkyezZs3+X0yqev4OyH3v++edz8sknZ8eOHfWeCh/QT37yk4wbNy6vv/56KpVKPvaxj+WrX/1qBg8enCT52c9+ltbWVsf6APH9738/F110UY444oj88pe/zOLFi3PllVfmpJNOSrVazdKlS/PYY4+JlAPEIYcckpNOOmmXe8gtXbo0p5xySvr165dKpZInn3yyPhMslEAp2Le//e333P/qq69m6tSpfmkdACZOnJhf/epXeeCBB/KLX/wiU6ZMyZo1a/KDH/wgRx99tEA5wIwaNSrnnHNO7rrrrixatCg33HBDrr/++vzd3/1dkmT69OlZuXJlHn/88TrPlJ4wc+bM3HffffnHf/zHbtHZp0+fPP/887ucMeXXBErBDjnkkFQqlfe8kKpSqfildQBobm7OE088kREjRtS23XjjjfnOd76Tp556Kv369RMoB5CmpqasXr06xx57bHbu3JmGhoY888wzOfnkk5Mka9asyZgxY3a54zv7r5UrV+aKK67IhRdemJkzZ6ZPnz4C5X24SLZggwcPzje+8Y3s3Llzt8u//du/1XuK9JBt27ald+/ul4T9wz/8QyZMmJCzzjorP/nJT+o0M/a1Qw45JIcddli30/+NjY3ZvHlz/SZFjzv11FOzevXqvPHGGznllFPywgsvpFKp1HtaRRMoBRs5cuR7Rsj7nV1h/3H88cdn1apVu2yfO3duLrrookyYMKEOs2JfOeaYY/LKK6/U1n/84x/n6KOPrq2vW7eudv0RB44jjjgiDz74YKZNm5axY8c6I/o+BErBbr311owaNepd9x977LF56qmnfoczYl+ZOHFivvrVr+5237x583LZZZeJ0QPI9ddf3+2X0/Dhw7udQfve977nAtkD2KWXXppVq1blm9/8ZoYMGVLv6RTLNSgAQHGcQQEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgXYa4888khGjBiRww8/PAMHDsyYMWPy1ltvJUkeeOCBnHDCCTnssMNy/PHH50tf+lK3x/7t3/5tjjvuuPTt2zd/+Id/mNtvvz1vv/12bf/zzz+fs88+O42Njenfv39GjhzZ7a/tfuMb38iHP/zhNDQ05Jhjjsk999zT7fmPOeaYzJgxI3/1V3+VxsbGHH300fnyl7+8D38aQE/q/f5DAHa1YcOGXHbZZZk1a1YmTpyYLVu25Ic//GGq1Wruu+++3HHHHZk3b14+8pGP5Nlnn821116bfv365aqrrkry6/vNLFiwIK2trXnhhRdy7bXXprGxMbfddluS5JOf/GQ+8pGPZP78+enVq1eee+659OnTJ0myevXqXHzxxWlvb88ll1yS5cuX54YbbsjAgQNz9dVX1+Z4zz335M4778xnP/vZPPLII7n++uvz8Y9/PMcff/zv/OcF7KEqwF5YvXp1NUn1tdde22VfW1tbdeHChd223XnnndUzzjjjXZ9v1qxZ1ZEjR9bWGxsbqwsWLNjt2Msvv7w6duzYbttuvfXW6oknnlhbHzJkSPWKK66ore/cubN61FFHVefPn//ebwwogo94gL1y0kkn5dxzz82IESPyF3/xF7nvvvuyadOmvPHGG1m3bl2uueaaHHHEEbXlrrvuyn/913/VHv/II4/kox/9aFpaWnLEEUfk9ttvz+uvv17bP2XKlHzqU5/KmDFjcvfdd3d77EsvvZQzzzyz23zOPPPMvPzyy93ucfMnf/IntX9XKpW0tLRk48aN++LHAfQwgQLslV69emXJkiX53ve+lxNPPDFz587NH//xH+fVV19Nktx333157rnnasuaNWuyYsWKJMmKFSty6aWXZvz48fnOd76TZ599NtOnT8/27dtrz9/e3p4XX3wx559/fp588smceOKJWbx4cZKkWq3ucqv66m5uK/abj4R+o1KpZOfOnT36cwD2DdegAHutUqnkzDPPzJlnnpnPfe5zGTJkSH70ox/lD/7gD/Lqq6/mk5/85G4f96Mf/ShDhgzJ9OnTa9v++7//e5dxxx13XI477rh8+tOfzmWXXZYHHnggEydOzIknnphly5Z1G7t8+fIcd9xx6dWrV8++SaAuBAqwV5555pn867/+a8aNG5ejjjoqzzzzTN54442ccMIJaW9vz9/8zd+kf//+GT9+fLq6urJq1aps2rQpU6ZMybHHHpvXX389ixYtyqmnnppHH320dnYkSbZt25Zbb701f/7nf56hQ4dm/fr1WblyZT7xiU8kSaZOnZpTTz01d955Zy655JL8+Mc/zrx583b5phCw/xIowF7p379/nn766cyZMyednZ0ZMmRI7rnnnowfPz5J0rdv33zxi1/Mbbfdln79+mXEiBGZPHlykuSiiy7Kpz/96dx0003p6urK+eefn9tvvz3t7e1Jfv3x0Ztvvpkrr7wyP/vZzzJo0KBMmjQpn//855MkJ598cr7+9a/nc5/7XO68884MHjw4X/jCF7p9gwfYv1Wqu/vgFgCgjlwkCwAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBx/g/lr16VgP7gtAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # 查看 不同季节下  用车辆的平均值\n",
    "#一。pandas统计和绘制\n",
    "g = all_df.groupby('season')\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "df4 = g.apply(mean_numeric_columns)  # 对每个分组应用函数计算均值\n",
    "s2=df4['count']\n",
    "print(\"s2\")\n",
    "print(s2)\n",
    "s2.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "98082a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9VoMHDw7YcAAAAM3VpLexli9frrS0NPXo0UP9+/eXZVnav3+/XC6XtmzZEugZAQAAmqxJsZOUlKTDhw9r3bp1+te//iXbtnXffffpgQceUGhoaKBnRAs406HLOX8GAMA0TYqdrKwsRUdHa/LkyX7bX3nlFZWVlWnOnDkBGQ4tp/rqNKdHAADgkmjSPTsvvfSS+vTp02j7tddeq9/85jfNHgoAACBQmhQ7Xq9XMTExjbZHRkaqpKSk2UMBAAAESpNiJy4uTjt27Gi0fceOHYqNjW32UAAAAIHSpHt2HnnkEaWnp+vUqVMaMWKEJOm9997T7Nmz+QvKAAAgqDQpdmbPnq2vv/5aU6ZMUX19vSSpU6dOmjNnjubOnRvQAQEAAJqjSbFjWZZ+/etfa/78+fr4448VGhqqXr16yeVyBXo+AACAZmlS7Hyna9euGjhwYKBmAQAACLgm3aAMAADQWhA7AADAaMQOAAAwmqOxs23bNo0ePVqxsbGyLEsbN27022/btjIzMxUbG6vQ0FAlJyfr0KFDfsfU1dVp+vTp6t69u7p06aI777xTR48evYRnAQAAgpmjsVNTU6P+/fsrJyfnnPsXL16spUuXKicnR7t375bH49GoUaNUVVXlOyY9PV35+fnKy8vT9u3bVV1drTvuuEMNDQ2X6jQAAEAQa9ansZorLS1NaWnn/kJK27a1fPlyzZs3T2PHjpUkrV27VtHR0Vq/fr0effRRVVRU6OWXX9Zrr72mlJQUSdK6desUFxend999V7fddtslOxcAABCcgvaenaKiInm9XqWmpvq2uVwu3Xrrrdq5c6ckae/evTp16pTfMbGxsUpMTPQdcy51dXWqrKz0WwAAgJmCNna8Xq8kKTo62m97dHS0b5/X61XHjh112WWXnfeYc8nKylJ4eLhviYuLC/D0AAAgWARt7HzHsiy/ddu2G2072/cdM3fuXFVUVPiW4uLigMwKAACCT9DGjsfjkaRGV2hKS0t9V3s8Ho/q6+tVXl5+3mPOxeVyKSwszG8BAABmCtrYSUhIkMfjUUFBgW9bfX29CgsLNWTIEEnSgAED1KFDB79jSkpKdPDgQd8xAACgbXP001jV1dX69NNPfetFRUXav3+/IiIi1LNnT6Wnp2vRokXq1auXevXqpUWLFqlz586aMGGCJCk8PFyTJk3SzJkz1a1bN0VERGjWrFlKSkryfToLAAC0bY7Gzp49ezR8+HDfekZGhiRp4sSJWrNmjWbPnq3a2lpNmTJF5eXlGjRokLZs2SK32+17zLJly9S+fXuNGzdOtbW1GjlypNasWaOQkJBLfj4AACD4OBo7ycnJsm37vPsty1JmZqYyMzPPe0ynTp2UnZ2t7OzsFpgQAAC0dkF7zw4AAEAgEDsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMFpQx05mZqYsy/JbPB6Pb79t28rMzFRsbKxCQ0OVnJysQ4cOOTgxAAAINkEdO5J07bXXqqSkxLccOHDAt2/x4sVaunSpcnJytHv3bnk8Ho0aNUpVVVUOTgwAAIJJ0MdO+/bt5fF4fEtkZKSkb6/qLF++XPPmzdPYsWOVmJiotWvX6ptvvtH69esdnhoAAASLoI+dw4cPKzY2VgkJCbrvvvv0+eefS5KKiork9XqVmprqO9blcunWW2/Vzp07L/icdXV1qqys9FsAAICZgjp2Bg0apFdffVXvvPOOVq1aJa/XqyFDhuj48ePyer2SpOjoaL/HREdH+/adT1ZWlsLDw31LXFxci50DAABwVlDHTlpamu655x4lJSUpJSVFmzZtkiStXbvWd4xlWX6PsW270bazzZ07VxUVFb6luLg48MMDAICgENSxc7YuXbooKSlJhw8f9n0q6+yrOKWlpY2u9pzN5XIpLCzMbwEAAGZqVbFTV1enjz/+WDExMUpISJDH41FBQYFvf319vQoLCzVkyBAHpwQAAMGkvdMDXMisWbM0evRo9ezZU6WlpVq4cKEqKys1ceJEWZal9PR0LVq0SL169VKvXr20aNEide7cWRMmTHB6dAAAECSCOnaOHj2q+++/X1999ZUiIyN10003adeuXYqPj5ckzZ49W7W1tZoyZYrKy8s1aNAgbdmyRW632+HJAQBAsAjq2MnLy7vgfsuylJmZqczMzEszEAAAaHVa1T07AAAAF4vYAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRjImdF198UQkJCerUqZMGDBigDz74wOmRAABAEDAidl5//XWlp6dr3rx52rdvn2655RalpaXpyJEjTo8GAAAcZkTsLF26VJMmTdIjjzyia665RsuXL1dcXJxyc3OdHg0AADisvdMDNFd9fb327t2rX/7yl37bU1NTtXPnznM+pq6uTnV1db71iooKSVJlZeX//O821NU2YVq0lIt57Zqi6mRDiz4/Lk5Lv96na0+36PPj4rT0611zmtc7mFzM6/3dsbZtX/C4Vh87X331lRoaGhQdHe23PTo6Wl6v95yPycrK0rPPPttoe1xcXIvMiJYXnv0Lp0fApZQV7vQEuITC5/B6tynhF/96V1VVKfwCj2v1sfMdy7L81m3bbrTtO3PnzlVGRoZv/cyZM/r666/VrVu38z7GRJWVlYqLi1NxcbHCwsKcHgctjNe7beH1blva6utt27aqqqoUGxt7weNafex0795dISEhja7ilJaWNrra8x2XyyWXy+W37Qc/+EFLjRj0wsLC2tR/HG0dr3fbwuvdtrTF1/tCV3S+0+pvUO7YsaMGDBiggoICv+0FBQUaMmSIQ1MBAIBg0eqv7EhSRkaGHnzwQd14440aPHiwVq5cqSNHjugXv+A+DgAA2jojYmf8+PE6fvy4nnvuOZWUlCgxMVFvvfWW4uPjnR4tqLlcLj3zzDON3tKDmXi92xZe77aF1/vCLPv7Pq8FAADQirX6e3YAAAAuhNgBAABGI3YAAIDRiB0AAGA0YqcN2rZtm0aPHq3Y2FhZlqWNGzc6PRJaSFZWlgYOHCi3262oqCjdfffd+uSTT5weCy0oNzdX/fr18/1xucGDB+vtt992eixcAllZWbIsS+np6U6PEnSInTaopqZG/fv3V05OjtOjoIUVFhZq6tSp2rVrlwoKCnT69GmlpqaqpqbG6dHQQnr06KHnn39ee/bs0Z49ezRixAjdddddOnTokNOjoQXt3r1bK1euVL9+/ZweJSjx0fM2zrIs5efn6+6773Z6FFwCZWVlioqKUmFhoYYNG+b0OLhEIiIi9MILL2jSpElOj4IWUF1drRtuuEEvvviiFi5cqOuuu07Lly93eqygwpUdoA2pqKiQ9O3//GC+hoYG5eXlqaamRoMHD3Z6HLSQqVOn6vbbb1dKSorTowQtI/6CMoDvZ9u2MjIydPPNNysxMdHpcdCCDhw4oMGDB+vkyZPq2rWr8vPz1bdvX6fHQgvIy8vT3//+d+3evdvpUYIasQO0EdOmTdNHH32k7du3Oz0KWtjVV1+t/fv368SJE3rjjTc0ceJEFRYWEjyGKS4u1owZM7RlyxZ16tTJ6XGCGvfstHHcs9M2TJ8+XRs3btS2bduUkJDg9Di4xFJSUvTDH/5QL730ktOjIIA2btyoMWPGKCQkxLetoaFBlmWpXbt2qqur89vXlnFlBzCYbduaPn268vPztXXrVkKnjbJtW3V1dU6PgQAbOXKkDhw44Lft5z//ufr06aM5c+YQOv8PsdMGVVdX69NPP/WtFxUVaf/+/YqIiFDPnj0dnAyBNnXqVK1fv15vvvmm3G63vF6vJCk8PFyhoaEOT4eW8NRTTyktLU1xcXGqqqpSXl6etm7dqs2bNzs9GgLM7XY3uv+uS5cu6tatG/flnYXYaYP27Nmj4cOH+9YzMjIkSRMnTtSaNWscmgotITc3V5KUnJzst3316tV66KGHLv1AaHHHjh3Tgw8+qJKSEoWHh6tfv37avHmzRo0a5fRogGO4ZwcAABiNv7MDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOgKCwYcMGJSUlKTQ0VN26dVNKSopqamokffvFpddcc406deqkPn366MUXX/R77Jw5c9S7d2917txZV155pebPn69Tp0759v/jH//Q8OHD5Xa7FRYWpgEDBmjPnj2+/W+88YauvfZauVwuXXHFFVqyZInf819xxRVatGiRHn74YbndbvXs2VMrV65swd8GgEDiW88BOK6kpET333+/Fi9erDFjxqiqqkoffPCBbNvWqlWr9MwzzygnJ0fXX3+99u3bp8mTJ6tLly6aOHGiJMntdmvNmjWKjY3VgQMHNHnyZLndbs2ePVuS9MADD+j6669Xbm6uQkJCtH//fnXo0EGStHfvXo0bN06ZmZkaP368du7cqSlTpqhbt25+3wy/ZMkSLViwQE899ZQ2bNigxx57TMOGDVOfPn0u+e8LwEWyAcBhe/futSXZX3zxRaN9cXFx9vr16/22LViwwB48ePB5n2/x4sX2gAEDfOtut9tes2bNOY+dMGGCPWrUKL9tTz75pN23b1/fenx8vP3Tn/7Ut37mzBk7KirKzs3NvfCJAQgKvI0FwHH9+/fXyJEjlZSUpHvvvVerVq1SeXm5ysrKVFxcrEmTJqlr166+ZeHChfrss898j9+wYYNuvvlmeTwede3aVfPnz9eRI0d8+zMyMvTII48oJSVFzz//vN9jP/74Yw0dOtRvnqFDh+rw4cNqaGjwbevXr5/vZ8uy5PF4VFpa2hK/DgABRuwAcFxISIgKCgr09ttvq2/fvsrOztbVV1+tzz//XJK0atUq7d+/37ccPHhQu3btkiTt2rVL9913n9LS0vTnP/9Z+/bt07x581RfX+97/szMTB06dEi333673n//ffXt21f5+fmSJNu2ZVmW3zy2bTea8bu3vb5jWZbOnDkT0N8DgJbBPTsAgoJlWRo6dKiGDh2qp59+WvHx8dqxY4cuv/xyff7553rggQfO+bgdO3YoPj5e8+bN8237z3/+0+i43r17q3fv3nriiSd0//33a/Xq1RozZoz69u2r7du3+x27c+dO9e7dWyEhIYE9SQCOIHYAOO7DDz/Ue++9p9TUVEVFRenDDz9UWVmZrrnmGmVmZurxxx9XWFiY0tLSVFdXpz179qi8vFwZGRm66qqrdOTIEeXl5WngwIHatGmT76qNJNXW1urJJ5/UT37yEyUkJOjo0aPavXu37rnnHknSzJkzNXDgQC1YsEDjx4/XX//6V+Xk5DT6xBeA1ovYAeC4sLAwbdu2TcuXL1dlZaXi4+O1ZMkSpaWlSZI6d+6sF154QbNnz1aXLl2UlJSk9PR0SdJdd92lJ554QtOmTVNdXZ1uv/12zZ8/X5mZmZK+fYvs+PHj+tnPfqZjx46pe/fuGjt2rJ599llJ0g033KDf//73evrpp7VgwQLFxMToueee8/skFoDWzbLP9eY0AACAIbhBGQAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNH+D/uv5mugPeCUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#二。seaborn绘制\n",
    "import seaborn as sns\n",
    "sns.barplot(data=all_df,x='season',y='count')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9c65e03c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#barplot 和 countplot的区别\n",
    "#以上 barplot 是 先根据 一个属性分组 然后 对组内 另一个属性 进行 求均值\n",
    "#countplot是频数统计 即 value_counts()\n",
    "sns.countplot(data=all_df,x='season')\n",
    "plt.show()\n",
    "s3=all_df['season'].value_counts()\n",
    "s3.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e3f15928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s2\n",
      "month_num\n",
      "1      90.366516\n",
      "2     110.003330\n",
      "3     145.399108\n",
      "4     177.013363\n",
      "5     212.294118\n",
      "6     231.093855\n",
      "7     225.133929\n",
      "8     218.130631\n",
      "9     213.777273\n",
      "10    205.184510\n",
      "11    193.677278\n",
      "12    174.349451\n",
      "Name: count, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #分析月份对用车辆的影响\n",
    "#（求 不同月份，用车数量的平均值，使用柱状图展示）\n",
    "# #一。pandas统计和绘制\n",
    "g = all_df.groupby('month_num')\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "df4 = g.apply(mean_numeric_columns)  # 对每个分组应用函数计算均值\n",
    "s2=df4['count']\n",
    "print(\"s2\")\n",
    "print(s2)\n",
    "s2.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d675686b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(1, 0): [1067, 5875, 997, 5637, 5995, 0, 877, 5971, 5947, 6426, 6665, 348, 6306, 1136, 478, 501, 548, 5517, 233, 5469, 6761, 6689, 5708, 6162, 1091, 6713, 277, 6042, 6282, 92, 688, 5827, 6498, 6234, 666, 324, 6737, 6114, 5732, 6570, 1206, 6138, 5851, 829, 6546, 301, 5613, 782, 255, 6354, 1043, 6090, 949, 5565, 1020, 5589, 5422, 6378, 69, 6018, 1299, 973, 431, 524, 596, 6522, 24, 901, 6474, 1160, 5923, 853, 6450, 47, 6617, 1252, 925, 6641, 572, 1113, 5446, 138, 5541, 6593, 1276, 6330, 805, 372, 115, 5899, 5684, 6258, 712, 5660, 6402, 643, 5493, 735, 209, 1183, ...], (1, 1): [349, 5972, 5661, 5757, 93, 5852, 278, 6307, 5948, 573, 806, 1137, 950, 1253, 139, 783, 1, 974, 5685, 5828, 186, 713, 48, 5447, 1184, 1114, 6235, 5542, 1230, 878, 5924, 479, 6187, 6642, 597, 830, 6618, 5566, 210, 736, 6211, 5733, 6091, 6139, 5423, 409, 6427, 6499, 6547, 1092, 1161, 6283, 6355, 5518, 5900, 6475, 70, 6331, 432, 549, 6571, 6451, 6115, 1207, 5614, 502, 5494, 325, 373, 6259, 644, 5996, 760, 6666, 6738, 6067, 6523, 1277, 5876, 1021, 5590, 5638, 621, 6714, 1300, 854, 25, 6043, 455, 6762, 1068, 6019, 6690, 6594, 926, 6163, 667, 689, 5781, 5470, ...], (1, 2): [714, 831, 5639, 6619, 6308, 5543, 668, 6428, 6643, 5424, 6116, 26, 645, 1093, 622, 503, 5949, 5686, 6452, 6595, 6380, 6020, 1301, 5997, 5758, 6667, 117, 5591, 5782, 163, 574, 6236, 5495, 999, 5448, 737, 6691, 5615, 903, 1278, 6188, 6260, 1138, 5925, 807, 1185, 6284, 6164, 187, 1069, 211, 410, 6212, 6356, 5662, 6332, 257, 1208, 6092, 761, 5519, 6404, 598, 6739, 1045, 6476, 879, 2, 6763, 71, 5853, 6140, 6068, 350, 303, 5829, 6715, 855, 1115, 140, 5710, 5877, 6524, 6500, 5901, 433, 5734, 480, 690, 6044, 975, 951, 326, 5973, 94, 550, 279, 456, 927, 6548, ...], (1, 3): [6333, 6620, 6285, 691, 1302, 623, 6093, 27, 5854, 5759, 6189, 375, 715, 6644, 1139, 738, 481, 527, 1231, 6141, 6045, 1046, 5663, 6740, 856, 808, 5472, 928, 6692, 5520, 551, 952, 1186, 5425, 6549, 6764, 6261, 904, 6213, 5926, 6429, 188, 6381, 5950, 6117, 880, 1162, 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     ]
    }
   ],
   "source": [
    "##对 训练集中 数据 ,根据 季节 和 小时 进行分组（双属性分组）\n",
    "g=all_df.groupby(['season','hour'],sort=True)\n",
    "print(g.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7eaf6c49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s5是双列 行索引\n",
      "season  hour\n",
      "1       0        28.292035\n",
      "        1        18.761062\n",
      "        2        13.205607\n",
      "        3         7.760417\n",
      "        4         3.336634\n",
      "                   ...    \n",
      "4       19      295.433628\n",
      "        20      214.561404\n",
      "        21      162.877193\n",
      "        22      126.412281\n",
      "        23       89.298246\n",
      "Name: count, Length: 96, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#组内求均值\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "df5 = g.apply(mean_numeric_columns)  # 对每个分组应用函数计算均值\n",
    "s5=df5['count']\n",
    "print(\"s5是双列 行索引\")\n",
    "print(s5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "baad6738",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "重置索引后的数据帧df6\n"
     ]
    },
    {
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       "      <td>1</td>\n",
       "      <td>18.761062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13.205607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7.760417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.336634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>295.433628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>214.561404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>162.877193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>126.412281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>89.298246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    season  hour       count\n",
       "0        1     0   28.292035\n",
       "1        1     1   18.761062\n",
       "2        1     2   13.205607\n",
       "3        1     3    7.760417\n",
       "4        1     4    3.336634\n",
       "..     ...   ...         ...\n",
       "91       4    19  295.433628\n",
       "92       4    20  214.561404\n",
       "93       4    21  162.877193\n",
       "94       4    22  126.412281\n",
       "95       4    23   89.298246\n",
       "\n",
       "[96 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #重置索引 使得 原来的双索引，变成 属性 列\n",
    "df6=s5.reset_index()\n",
    "print(\"重置索引后的数据帧df6\")\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "71bd50ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s8zArOxISi85fRKBbYKPLBvgMYNn0ZWzM2Miru181WnPOotPreP/kT/zUuxe3lpbxi5dHg4KFK3x9iNLpWJRfRERtLRz+tccqO/szSpq06FiTlFlKYkYJ8eF+LQs7OMKNJRAIBIKup7IQ8o3xMkednclwNj+LxwfHN6no1CNJElPDp/LjlT8yf8R8XNVKheaMsxMvB/o33WvL1ZXbeoWQ5uwM1SVtey8OzNqDuS0LWfCXnfKOilB2BAKBQND1pG83/bjewqqDLHOldhh5b7xJ9vMLyXvjTaoTE5vMGNI4abg37l5+uuonpoVPs2sL5WoVzwb5I2t8WvUWugOl1bUdKu+oCDeWQCAQCLoei3iddR5uAPTJl5n/q56InE8ptBAtXL4cTXQ0YUsW4xoZ2ejpenv2ZumUpSScSeD5v58kR1dk0zZSXV1JCRtObKvfiGPj4+bcofKOirDsCAQCgaDrOW0MLj7t5MQxFxf65Mu8uFJPRE7j4trUVE7dfAu6Y8eaPe2E3hOYNegKu7ayQZttl3x3YsbwELvkZ9opbwsVFRUkJiaSmJgIwMmTJ0lMTCQ9Pb3dr1WPUHYEAoFA0LVoyyAnGYD1Hm4gGy06ntrmlxnKysh66ukWi+BVtKXXVg8jvq8vsX1sc9PF9fFhRF/fdt/Dnj17iI+PJz4+HoBHHnmE+Ph4nnvuuXa/Vj1C2REIBAJB15KxC2RjP6z17u5EZtGkRccabUoK2uTkZmW8Xbzt2o698t0JSZJ4fXYc3prmo1i8NU68NjuuQ9LOJ0+ejCzLDV6ffPJJu1+rHqHsCAQCgaBrOevCylGrSda4MvqYfY1Ay9etb/b4lPApdp1vavhUu+S7G4NDvPju3glNWnji+vjw3b0TGBzi1ck76zhEgLJAIBAIupazwcnrzwYmt+S+skZf1rzbKTYwlqiAKA4UHrDpfAlnEogN6qkhykYGh3jx030TScwo4a+DuZRW1+Lj5szM4SGM6OvbIwoJWiKUHYFAIBB0HbXVxorFmKsmV2jsO4Xau3m3kyRJLJq4iNvW3EZ5TcstEN5Leo8aQw0PxD/Q4276lkiSRHy4X48oGtgSwo0lEAgEgq4jcw8YailSqdirMRb82xNp363Ja3rL9XQi/CL47OLPiAqIavS4m0HpOluespzX9rzW4R3ABZ2DsOwIBAKBoOs468La5O6G4awV5VgYlAwIxPdkQYvLNTExaGJtczlF+EWw6tJVpBSksCF9A2U1ZcZeW8m/MCB9L/eHBrFPYzYrfX7wc2r0NTw17ilUkrANdGeEsiMQCASCruNscLJlLywkCbeFT8BdT4Je3/RaSSL43/+yy9UkSRKxQbHKmBynUDi9h2U5+TwYEsRON7PC8/WRr9HpdSwcvxC1Sm3zdQSOhVBVBQKBQNA11NVAxi7KJYkdFgpGoFsgw8JHNa/oAMgyRR993HZX07ArwNUHd1nm3dx8LqhSRkj/mPYjT259klpDz2idcC4ilB2BQCAQdA3ZSVBXzd/ubtRaWGem9p1K5VplOrkmPh7f66/H+4rLwUK2Yv16Sr7+um37cHGHmOuM15Fl3s7NY5rnAIXIHyf/4PHNj1OrFwpPd0QoOwKBQCDoGtKN8ToKFxYwrd80yteuVcz1evYZer2wkN6vvkrgffcpjuUuebnFthEtMvI204/OwGs5OczqP0shsj59PQ9tfAidXte2awk6HaHsCAQCgaBrOJ2AVpLYauHC8nLxIt55IFV795rmnPv0wXXYMNM48J/34DZypGks63ScefQxDLo2KCFhIyA0xnzN/CMsGXAtV0VcpRD7+8zf3Lf+Pqpqq1p/LUGnI5QdgUAgEHQ+Bj2c3s42Nw3VKvOtaErfKWg3bgGLVHCvmTMVQciSkxO9X3sVlZe5wq/u6FHyXnu9bXsaebtiqN7/OS9MeIHrh1yvmN+ZvZN7191LRU1F267X1cgyZOyGdQvhlwXGfzN2G+c7iCVLljBmzBi8vLwIDg7mqquu4siRIx12vXqEsiMQCASCzifvIOhKTYUE65kWPo3yv5QuLK8Z0xssd+7dm14vvqCYK165kvKNG1u/p5jrQO1qHqf+gKqmkqfHPc1tw29TiO7L28c/1v6DUl1p66/XleQdguVTYMV02PoW7P3Y+O+K6cb5vEMdctnNmzdz3333sWPHDtauXUtdXR0zZ86kstK+Zq32IpQdgUAgEHQ+pxOoxVhfpx43JzfGecZQuWOHac4pOBi3uLhGT+E9axY+116jmMt+6mlq8/Jatyc3Pxh+pXlcWwkHViNJEo+Nfox5MfMU4ikFKdz9190UVheSlJ/E0r1LeXH7iyzdu5Sk/CTHLUiYdwg+ugiy9jd+PGu/8XgHKDxr1qzhjjvuICoqiri4OD7++GPS09PZa+G27AiEsiMQCASCzuf0Nna7aShXm29D5/c+n7q/E6CuzjTnNX06kqrpW1XoU0/h0r+/aawvLib73/9GNtjXTNTEyFuV432fA8b6PA+OfJAH4h9QHD5cdJiLvr+IW36/hRWpK/j26LesSF3BLb/fwo2/3UhacVrr9tFRyDL8eC9oW7BIaUvhx/kd6tICKC017sPf379DryOUHYFAIBB0LrIMpxNYZ2HVAZgePp0yaxfWzJnNnkrl4UHYG6+Ds7NprjJhO0Uff9y6vfU7H/ws0s4zd0HeYdPwH7H/4LHRjymWNJWddaDwALetuc2xFJ7MPU1bdKzJ2mfqW9YRyLLMI488wvnnn090dHSHXQeEsiMQCASCzqYwDX1lPhss4nWcVc6c7z+ayq1bTXNqX1/cR49q8XRuUVEEP/ywYi7vraVUp6TavzeVCuJvUc7t/1wxvD3qdp4e+7RNpyuvKefZbc86jkvryG/2yR/+tWP2Adx///0kJyezatWqDrtGPULZEQgEAkHncnobSa6uFDqZ2y+MDxsPO/YhW6SPe06fhuRkW1cj/ztux2PiRPNEXR1Zjz2GoTWBryNuBsteWEmrjNWeLRgWOAxbSS1MJaUgxf59dATVJR0rbyMPPPAAP//8Mxs3bqRPnz4dcg1LhLIjEAgEgs7ldALrPBq6sKyzsLxnzLD5lJJKRdjLS1BbxH7UnD5Nzn8W278/714QeZF5XFUIR35XiGxMty/ra0P6Bvv30RG4+XasfAvIssz999/PDz/8wIYNGxgwYEDLi9oBoewIBAKBoFORTycoUs5VkopJweOp2LTJPOfpifv48Xad1ykoiLAlSuWm9IcfKPv99yZWNEODQOXPFMOymjK7TmevfIcx5FL75Ide1q6Xv++++1i5ciVffvklXl5e5OTkkJOTQ3V1dbtexxqh7AgEAoGg8yhJ51B1DlnOZvfU6JDROO87hKHKXJXYc8oUVC4udp/ec9Ik/G5TKirZzz1PTeYZ+04UORM8Q8zj4xugJMM09Hbxtut09sp3GH1GQ1i8bbJhI6F3yzFT9rBs2TJKS0uZPHkyvXr1Mr2+bmt/sxYQyo5AIBAIOo/TCQ17YdlYSNBWgh99FNehQ01jQ0UFWY8/jmyR0t4iameIu9FiQobEL0yjKeFT7NrT1PCpdsl3GJIEVy0DjU/zchofuOp9RdPV9kCW5UZfd9xxR7texxqh7AgEAoGg8zi9jXVWVZOn9LqQ8g3mmBZJo8HzggtafQmVqyu9X38NSWPuuVW9fz8F7y+z70QjlVWT2b/S2OYCiA2MJSogyqbTRAdEExMY07JgZxE8DO76s2kLT9hI4/Fg24OwHR2h7AgEAoGg0ziRsY2TLuaaOLEBUXgfTMdQai5y53nBBajc3BpbbjOuERGE/PvfirmC//s/qvbssf0kAYOgn0WGV2kGnNgEGIsMLpq4CC8Xr8bXnsXLxYuXJr6k6O3lEAQPg3kb4e71cP7DMOpO4793r4d5G3qUogMOpOwsWbIESZJYsGCBaU6WZRYuXEhYWBhubm5MnjyZAwcOKNbpdDoeeOABAgMD8fDw4IorriAzM7OTdy8QCASCFinPZV1NvmJqWv+ZlP31l2KupUKCtuJ7/Ry8LDO6DAbOPP4E+lI7+llZW3csApUj/CL47OLPmrTw9PbszWcXf0aEX4Q92+48JMkYwzN9IVy+1Phvn9Ht7rpyBBxC2dm9ezcffvghsbGxivlXX32VN998k3fffZfdu3cTGhrKjBkzKC8vN8ksWLCA1atX89VXX7F161YqKiq47LLL0Ov1nf02BAKBQNAc6Q3jdab2nkz5uvXmCWdnPCdPapfLSZJEr5dexCnEHGhcl51N5oMPkfvGG2Q/v5C8N96kOjGx6aJ/w64AV4vg4sO/QWWhaRjhF8GqS1fxxSVfcMmASxRLYwJiHFfROcfocmWnoqKCm2++meXLl+Pn52eal2WZpUuX8vTTT3PNNdcQHR3Np59+SlVVFV9++SVg7KmxYsUK3njjDaZPn058fDwrV64kJSWFdevWddVbEggEAkEjnDmxjkOu5gyrSPdeBJ8oRl9QYJrzmDAetVfzriF7UPv6EvbqqwprRdXOnRQt/x8lX39N4fLlnLrhRk7NnoPu2LGGJ3Bxh5jZ5rGhFpK/UohIkkRsUCyLJi7Cw9nDNL8jZwd6g3jwdgS6XNm57777uPTSS5k+XRl5f/LkSXJycphpYc50dXVl0qRJJCQkALB3715qa2sVMmFhYURHR5tkBAKBQOAYrM/ZqRhPH3BxmwoJ2orHuLH4zL6uWRltaiqnbr6lcYWngSvr80YbZDqrnRkbOtY0LtGVcKio/TuHC+ynS5Wdr776in379rFkyZIGx3JycgAIsTA/1o/rj+Xk5ODi4qKwCFnLNIZOp6OsrEzxEggEAkEHUlXEeoPyu3bawEspX2uh7KjVeE6b1u6XlmUZ3YGDLcoZysrIeurphi6tsBEQapFNlX/I2FCzESaGTVSMt53ZZu92BR1Alyk7GRkZPPTQQ6xcuRKNRXqgNdYR7LIstxjV3pLMkiVL8PHxMb369u1r3+YFAoFAYBcFx9ex38KFFa52p++ZGmqzskxz7mPG4GT18NoeaJOS0FoltzQpm5KCNjm54YGRtyvH+z9rKANM6D1BMU7IEl4GR6DLlJ29e/eSl5fHqFGjcHJywsnJic2bN/Pf//4XJycnk0XH2kKTl5dnOhYaGkpNTQ3FxcVNyjTGk08+SWlpqemVkZHRpKxAIBAI2s6GtJ+RLR5CpwWPpmKtMrayLYUEm6N8vX19qRQB0/XEXAdqV/M49QfQVTQQ6+vVl3CvcNM4KT+J8pryBnKCzqXLlJ1p06aRkpJCYmKi6TV69GhuvvlmEhMTGThwIKGhoay1MHHW1NSwefNmJkwwas6jRo3C2dlZIZOdnU1qaqpJpjFcXV3x9vZWvAQCgUDQcawrUbqRpg+ZQ7l1yvn09o/XAdDbGarQqLybHwy/0jyuqYADqxtdPyHMfP/Ry3p2Ze+y6/qC9sepZZGOwcvLi+joaMWch4cHAQEBpvkFCxawePFiIiMjiYyMZPHixbi7u3PTTTcB4OPjw9y5c3n00UcJCAjA39+fxx57jJiYmAYBzwJBj0OWjXEDR36D6hJjd+Ihl/bYOhmC7ktp2Rl2SzWA8e8y2CARqevFqVOnTDJuI0bgHBLcIddX2/lA26T8yFsh5RvzeN9nDRuGAhN7T+SrI+aMrW1Z25jWr/1jkdqKLMskFySzMX0jZTVleLt4MyV8CrGBsR1WBHHZsmUsW7bM9H8fFRXFc889x6xZszrkevV0mbJjC0888QTV1dXMnz+f4uJixo0bx19//YWXRVriW2+9hZOTE3PmzKG6uppp06bxySefoFaru3DnAkEHk3cIfrwXsvYr57e+ZSwBf9WyHlcBVdB92Zz6OXWWLiz33lSsteqF1U6FBBvDa9pUCpcvt11+ehOKSb/zwW8AFJ80jjN3Qd5hCB6qEBsbOhYnlRN1BmMvroSsBJviTTuTtOI0ntn2DAcKlbFMK1JXEBUQxaKJizqkRlCfPn14+eWXiYgwnvvTTz/lyiuvZP/+/URF2dZ+ozVIcpOVlM4dysrK8PHxobS0VLi0BI5P3iH46CLQNlMFVuPT43rbCLovD35zMRurzV3HV0TeRuCrCegOHzbNDVq3Fpc+fTrk+rIsc2r2HLSpqS3KamJi6P/N100rJltehw0vmcfj74eL/tNA7K4/72J3zm7T+OerfmaAzwC7994YWq2WkydPMmDAgGYTfJoirTiN29bc1mwskZeLV6dVf/b39+e1115j7ty5jR5v7v3aev/u8jo7AoHADmTZaNFpTtEB4/Ef5zdaC0Qg6EyqaqtIqDJnXPnq9UR7XaBQdFyHD+swRQeMWb1hSxajauFhVuXtTdji/zRvgRlxM0gWt86kVVBX00DMMm4HHCcrS5Zlntn2TItB0+U15Ty77dmmK0u3A3q9nq+++orKykrGjx/fYdcBoewIBN2LzD0m15UMJLm6sNTPhxcD/Fjq50OSqwumr6asfXBmb1ftVCAAYGv6RnSS+YY5pU5N9U6lhaUjCgla4xoZSf8vVqKxihWtR9Jo6P/FSlwjI5s/kXcviLzIPK4qhCO/NxBz1Ho7yQXJDVxXTZFamEpKQUq77yElJQVPT09cXV355z//yerVqxk+fHi7X8cSoewIBN2JI78BkObszI1hIdwSFsoKXx++9fZiha8Pt4SFcmNYCGnOZ7tKH/61CzcrEMC6o98rxtP9oijrxHgdS1wjI+n/7Tf0//orAubNQ23ZokirRWVrmwrroOR9DWvuDPEfgr/G3zTek7uHGn1DC1BnszF9o13yG9LtS9u3hSFDhpCYmMiOHTu49957uf322zl4sOWij21BKDsCQXeiuoQ0Z2du6xXCAVfXRkUOuLpyW6+zCk91SefuTyCwoEZfw5b8RNPYw2BglM8EtEnmon0ugwbhOmhQp+1JkiTc4uIIfvQR/G68QXGsYssW204SORM8LWq5Hd8AJcp6bSpJpXBlVddVsy9vX6v33V6U1diXhm+vvC24uLgQERHB6NGjWbJkCXFxcbz99tvtfh1LhLIjEHQjZI0PzwT5U65u/qNbrlbxbJA/ssank3YmEDRkR/YOKuVa0/jCqmp0Jw0KmY4qJGgLnhdeqBjbrOyonSHuRosJGRK/aCA2sbfSlZVwpuvjdrxd7EvCsVe+NciyjE6n69BrCGVHIOhGJPca1qRFx5pUV1dSwjrWDy4QNMf6U0p31XSDK+VbldYN705yYTWGJiYGta+vaVyVsB1DjY2uJuvmoPtXglWH8/G9lEG327K6Pm5nSvgUu+Snhk9t1+s/9dRT/P3335w6dYqUlBSefvppNm3axM0339yu17FGKDsCQTdio67pBreNsUGb3UE7EQiap85Qx8Z0c9sFV4OB8Z7xVO01B8079+6N67CuK48gqdV4XHCBaWyoqqJ6r41B/QGDoJ+F5aY0A05sUoq4BTDM3/z+jhYfJb8qvy1bbjOxgbFEBdhWzyY6IJqYwJiWBe0gNzeXW2+9lSFDhjBt2jR27tzJmjVrmNHBQepC2REIuhGO4G8XCGxhX+4+imvN6c0TqrXU5QeCwezG8po5s8sL7XlOmqQYV2zabPtia+vOmn/DLwtg3ULI2A2y3NCV1cUp6JIksWjiIrxcmg/G9nLx4qWJL7X7/8+KFSs4deoUOp2OvLw81q1b1+GKDghlRyDoVjiiv10gaIx16comn9OrqilPVVo1vDrhJtcSHhMngMp8K7Q5bgdg2BXg7GEeFxyFvR8bK5mvmA7LpzDBTVk/yBFcWRF+EXx28WdNWniiA6I7raBgZ+HQ7SIEAoGSKeFTWJG6wmb59va3CwS2YJANrLdwYTnJMhdUu5Kzz1xfxyk4GLcRcV2xPQVOfn64xcVRvd9Yv6rm5Elq0tNxCQ9vYSVQchoMtU0fz9rPiJ8exr1PCFV6LQA7snZgkA2opK61NUT4RbDq0lWkFKSwIX2DqTfW1PCpxATGdLnFrb0Ryo5A0I2o97fbUhSsI/ztAkFz1DeW/Prw1+RV5ZnmR2u1qCqHQN1p05zX9OlIKsdwLnhOutCk7ABUbPkb/1taCJitr2beQu0cZ20pY2uC2HS2XWOxrphDhYeICuy4PlC2IkkSsUGxxAbFdvVWOhzH+EsTCAQ2IUkSi8Y9g8ZgaFauo/ztAkFTpBWnceNvN3LL77fwy4lfFMdOOzmTfVKZqeQ1s+tdWPU0SEHfbEPcjkU185aYWKxMLNh6ZqvNexO0D0LZEQi6GRE6HWO12iaPR7sG9Th/u8CxqW8s2ZTFsUhWozuQaRqrfX1xHz26s7bXIq7DhuEUFGQaV+3ciaG6uvlFZ6uZ28LEauXntauDlM9FhLIjEHQz5OxEDru4NHosuK6OL9X9hKIj6DRsaSw54oSMS5157DltKpKT40RRSJKExySzdUeuqaFy587mF9lRnbxvXR19VW6mcVJ+UouNOAXti1B2BIJuxumsneQ1caPIc3KiMCexczckOKexpbHkeYeVnbO7spBgU1i7sipbyspy87Xr/BPcepl+1st6dmXvsmu9oG0IZUcg6GbsLlB2jPZwcleMk6qzQVvamVsSnMO01FjSuU5m5HGzslPr5oL7+PHNrOgaPCZMAIuHiIrNW5BluekFQy616/wTB12mGDtCCvq5hFB2BILuhEHPbp2yVskNQ29UjJNcXSE7GYGgM2ipcGXMSRk3i4SlrLheqJpww3Ylak9P3EeNMo1rz5yh5vjxphf0GQ1h8badPGwkY6NuxEllVqYSshKaV6YE7YpQdgSCboRccIxdrs5nBzIxZ+DKdRXMW6Pnxk16Is/IJLq6QHZil+5TcO7QUuHKcUeUN/TicYM7cjttokE15c3NuLIkCa5aBi0129X4wFXv4+HiSXywWTk6U3GG02Wnm1koaE+EsiMQdCNOnt5EoZOaPvkyiz/R8+xndWg//oIZ+2Wu3i7zn8/0XP+VExX7RGqroHNorrGkWi8z+phZ2dE5QdSlt3TGtlqF5yQ7u6AHD4O7/mzawuM/0Hg82Ngfa0LYBMXhrnZlybJMdWIieW+8SfbzC8l7402qExM71eK0ZMkSJEliwYIFHXodoewIBN2I3Zlb6ZMv8+JKPRFN9AQdmAPpy5PRHTvWuZsTnJPEBsQQZVA3emx4uoyXRdb1iYEqYvo4Tsq5NS4DB+Lcu7dpXLV3L/qKiuYXBQ+DeRvh7vXG9hGWDJhkUnQAJoY5Tp8s3bFjnJo9h1M33Ejh8uWUfP01hcuXc+qGGzk1e06nfH/s3r2bDz/8kNjYji9qKJQdgaAbsav0GPN/1ePZdJkdACQdZP373yImQNDhSGf28lJWJqpG/tbGWrmwooNKkLL2ddbW7EaSJKV1p66Oym02KCSSZIzhuep9sIjL4fh6Y6XlswzxH4K/xt803p2zm5oWKjB3BLpjxzh18y1oU1MbPa5NTeXUzbd0qMJTUVHBzTffzPLly/Hz8+uw69QjlB2BoJsgGwwUZlY0adGxRnvgINpkEags6GCO/EalSsJgVa1bkmXGHrVQdlQyg0Iq4fCvnbxB+/Cwrqa8xY4u6K5eEG6RaVaSDoXmIGeVpFK4sqrrqtmfZ1sV5vZClmWynnwKQ1nzgeWGsjKynnq6wx6Y7rvvPi699FKmT5/eIee3Rig7AkE34XjWDgYft++Lp3zd+paFBIK2UF3Ct16eiqnxVdXcc6QKv0rznEeIDrWLbFcxvq7AY9w4JFdX07hiSwsp6NZETFOO05Td3xvE7Zzp3LgdbVJSkxadBrIpKR3ywPTVV1+xb98+lixZ0u7nbgqh7AgE3YRdx/9o0X1ljb6FpzeBoK2UurqzxsPDNHYzGHgjr4DrDiq7gXv3OfvHa2cxvs5G5eaG+7ixprE+vwDdoUO2nyDCylLRkrLTyUHK5es32Cffzg9MGRkZPPTQQ6xcuRKNRtOu524OoewIBN2EPXn7qLDzu0Ht3XxasEDQVn729KBGZXZhXVxZhadBpjzD4o9VkvHsfVbZGXoZjo7nhVYp6C1lZVkSEg2eIebxqa1Qa35KCXALYJi/OWj5aPFR8quUtbM6EnsfgNr7gWnv3r3k5eUxatQonJyccHJyYvPmzfz3v//FyckJvV7f8klagVB2BIJugEE2sLvqDHsi7fvIel04roN2JBAY4z++zdtpOcGc41qyd/lSW2UO1HULrMFJY4CwkdB7VCNnciwapKBvsiNuR5JgkIUrq64a0pVBztbWnc7MyrL3Aai9H5imTZtGSkoKiYmJptfo0aO5+eabSUxMRK1uPLOvrQhlRyDoBhwrPkYJeo6FQVqobWsyQ2Q0AR3zlCQQAOzJ3cPJ0pMA9MmXeevjOtR/+FJ6UtnCpLZSja7a15itZBXI7Ii49O2Ly4ABpnF1UhJ1xcW2n6BB3I7SFTSxtzIFvTNdWV7TptonP31ay0L2nM/Li+joaMXLw8ODgIAAoqOj2/ValghlRyDoBuzJ/Nv4gyTx/mVq9M7Ny1doYOllaqqz9nT85gTnLN8e/RbAVPupd27jikxdlROn1gehK3WcTuctoWgMKstUbrVDIRk0FbD4XVjF7YwIGoG7RU+7HVk7MMiGVu7UPjRxcWhsVCo0MTFoOqEGTmcglB2BoBuwK32T6efMIAn1iJAmZdN6wXO3qEkPVpFyZnsn7E5wLlKsLWbd6XUgyzbVfjJUVHZoKnN74zm5DXE77v7Qe6R5nH8YSjNNQ2e1M2N7mYOgi3XFHCq0Iwi6DUiSRNiSxahacE+pvL0JW/wfpE6wxG3atImlS5d26DWEsiMQODgG2cCe4sOmsbdej1tp46YdWZJ48QYVmUHGL6jEElFFWdAx/JT2E7WGWiKzsL32UwelMncEbqNGoXI3W18q//4b2Z7g2QZZWVaurLCuc2W5RkbS/4uVTVp4NDEx9P9iJa6RkZ22p45GKDsCgYNztPgoZQadaTyuQof2ZON3F0mWGXrGPE40VIC2tKO3KDjHMMgGkwtr9DH73C/dpfaTysUF9wnmAoH6khKq7VHUWkhBb6DsdHK9HdfISPp/+w39v/6KgHnz8L3+egLmzaP/11/R/5uve5SiA9B9HKgCwTnKruxdivGFZ/TItXWmsdrfH31RkWk8Kl1F4iDjz0murhiyElENVJrkBYK2sCtnF+nl6QA9uvaT54UXUmGhnFVs2YJ7fBNNP60JGwkaX9CWGMcnNoG+FtRGq2xf77709epLRnkGAMn5yVTUVODp4tno6ToCSZJwi4vDLS6u067ZVQjLjkDg4OzOUSo7QwuVfWT8b7tNMY7PND/DlKtVnDxtR9qsQGAD3x751vRzT6795GnVOqJysx1xO2onGGTREV5XBpnKhAHLFPQ6uY6dOTsRdAxC2REIHBi9Qc/enN2msa9ej3uBh0LGa+YMXCMjTOOgMzrcdOYg0ESL9QJBWymoLmBDurkK74FB9q1v71TmjsQ5NBTXoUNNY+3Bg9Tm5dl+gkFW7/V4C3E7drqyukuwd1tpj/cplB2BwIE5XHyY8roq03hMtQ5tprnhkNrXF5cBA3AfY87skGSZoRkWyk5FeudsVnBO8GPaj9TJZjfqcO8qNP62de7ujqnMDaw7f/9t++IW+mSN7TUWJ8lsiU3ISrDpxu7sbHSFVVVVtSDZM6h/n/XvuzWImB2BwIHZk6M0e5+fU4O+whwk4RYfjyRJuI8bR/GXX5rmY07L7D9r7ElEZwxS1vh0yp4FPReDbOC7o98p5maXVxA6WubUX8HNru3MVOb2xHPShRR++KFpXLF5C77XXmvbYu8wCI6CvAPGcdZ+qMgHzyAAPJw9GBE8gj25xs/5mYoznC47TX+f/s2eVq1W4+vrS95ZK5O7u3u3+73agizLVFVVkZeXh6+vb5uqKwtlRyBwYHZZxetEZ8hYJr+6jzLW8nAfO0YhNzJDxWdnfz7l4kxxegJ+g2d14E4F5wLbs7ZzpsKc7jdCq2NwbS2lpW7NrtPExBC2+D/dMsPHLS4Olbc3hrOB1ZXbtiHX1iLZamWImGZWdgBObITYOabhxN4TTcoOGFPQW1J2AEJDjaXU8+xxq3VTfH19Te+3tQhlRyBwUOoMdezN3Wsa++v1eBZ7U4rZzO020qjsOPn54Tp4MLqjRwHolaPHXaumSmN82ks+tZ5JQtkRtJFvjnyjGM8ur0CWoeiEP1j8XXpdfBFqH1/U3t54TZ+GJja221oeJCcnPM+fSNnvfwBgqKykat9+PCw6ozdLxDRI+K95nLZeoexMCJvA2/veNo0TshK4edjNLe9LkujVqxfBwcHU1ta2KN9dcXZ2bpd+WULZEQgclMNFh6msNcfnjK7WUp3vDRhr7kjOzmiiokzH3ceONSk7kgzDMmT2Rp4tLliQjEg+F7SFvKo8NmeaM/u89XpmVlahLXRGm29WdJz7hdP7zTeRVD0nJNRz0iSTsgNQsWWz7cpO+Hhwdofas/E1x9eDwQBnfz9D/Yfir/GnSGssH7E7Zzc1+hpc1C42nV6tVndY88yeRM/5axQIehjWLqwJRTXUFJmLC2piYlC5uprG1q6s4ekWQcrVNpa4FQia4IdjP6CXzU7UKyoq0cgyRce8FHL+N9/coxQdAI/zz1c0MK3YbEc5BydXGGAR5FyZDznm4oQqScX4MHPxwuq6avbn7W/TfgUN6Vl/kQJBD8Ja2YnNVJaqdx+pLG7mPkap7MSfNv+cqtJTW1WEQNAa9AY93x/7XjE3u7yC2moVZRnmeB3J3R2fq6/u7O11OE4BAWhiYkzjmrTj1GSeaWaFFfZWU+7E1hHnCkLZEQgckFpDLftzzU93gXV6vHOUXuf6eJ16nPz8cB0yxDTunSvjUW207mhVKo6m/d6BOxb0ZLae2UpOpdk6OKpay8DaOkrSPMBgtiD6XnUVai+vxk7R7WmYgm5HgcEGKejKejuWlh2AhDMJdu1N0DJC2REIHJCDhQepsqyvo9VSVeSukHFrpGy9u0UcgQQMzbRwZWXaUR9EILCgvg9WPbPLK5D1UHxCqdj43dJyYG13xXOSUtmpsKeasv9A8BtgHmfuUvSsC3QLZJj/MNP4SPERCqoLWr1XQUOEsiMQOCC7raoej63Qoi00f1xdBg7Eyc/PehkeY5VBk9GnLZSdosPW4gJBi2RXZPP3GbOi7KfXM6OyirIMN/TVZjmPiRNxHTiwC3bYOWiiolAHBJjGlTt2YNDa0RjM0pVlqIOTSmXJsnUEGLOyBO2HUHYEAgfEWtmJzzSARXNpt5GNNyN0Hz1aEUipUHZqRcyOwH6+P/Y9Btn8x3dleSUuQNExZduSnmzVAZBUKjwvuMA0lrVaqnbb0Yqlpbid3l3bBb2nI5QdgcDBqNXXKrIxguvq8M5Rppa6jxzV6Fq1r6+il094Hqa4nRwV5BQd64AdC3oqdYY6Vh9brZi7rryC6kJntIXm1Gjn8HA8J/X84gZtcmX1Px8s08nT1oNFa4gRQSNwdzK7qrdnbVcomYK2IZQdgcDBSC1MpbrO7B8Yo9VRXaCsuWGdiWWJpStLAoZb9slK+639Niro8WzO3ExetblC77hqLf3q6ig6amXVuenGHpdu3hgeEyaARU2bis2bbW9S6eoJ4eeZx6UZUGB++HBWOzM21PzZLdYVc6jwUJv3LDDS8/86BYJuhrULa0yVlmqLp2i1vz/O/fo1ud7dqthZlIUrKyl7ZzvtUnAu0CAwuaycumoVZRlmC4Tk5obvNdd09ta6BLWPD27xI0zj2owMak6dsv0ELbiyJvRWxu2IFPT2Qyg7AoGDYV1fZ1RuHYYa80fVfdTIZkvvW8ftKIoLlp1sx50KejKZ5ZmKFGh/vZ6pVdUUH3dXxI/5XHUlam/vLthh12DtrrOrwKC99XZE3E67IZQdgcCBqNHXkJSXZBqHNhKv4xY/0nqZArW3N5ph5jTW/nngVWVUeA7rKxUuMoGgKb4/9j2yRb+rq8srcNJDcZrSheV/c88OTLbG80KlslO5xY64neDh4NXLPD69DWrNn8dw73D6ePYxjZPzk6moqWj1XgVmhLIjEDgQKQUpaPXmdNYx1Tq0dsTrmGTGjVOMh52N26mT4ECWHRkkgnOSWn2tIjBZkmWuLa+gLNMNvdasfHtMGI9rRERXbLHLcB0ciZNFB+7K3XswVFY2s8ICSYJBFgUG67RGhccCy6ysOrmOnTnC9dweCGVHIHAgrF1YY7RaqvLNyo7k6opm+PAWz2PdJ8sybifx1No27lLQ09mQsYFCbaFpPKFaS986PcXWgcm33NLZW+tyJElSVlOuraVyxw7bT9BCNeUG9XZENeV2QSg7AoEDsSdnj2I8qqiG2kpzmwi3mBgkl5a7IbuPHm3qqgwQZRG3k5S3rx12KujJNFYxubrIWREo79ynzzmRbt4YDVLQN9kRtzNwMkgWt16ruJ2xoWNxksyf+d9P/s6L219k6d6lJOUn2Z79JVAglB2BwEHQ6XUk5iWaxmG1dfjmWsXrjGw+XqcetZeXwgIUnm+O20mszBJfmIImSS9LZ6dF1l5QXR0XVlU3tOrcdBOSWm29/JzA47zzkJydTeOKLVts/0y5+0Pv0eZxwVEoSTcNPV08GeJv7nFXUVvBt0e/ZUXqCm75/RZu/O1G0orT2vwezjWEsiMQOAjJ+cnUGGpMY2sXFjRdObkxrFPQ67OySqjjdNnpxpYIBHx39DvF+JrySiStirJ0i+7mbm74XntupJs3hsrDA/cxZldxXW4uuqNHbT9Bg6wssysrrTiNtJKmlZkDhQe4bc1tQuGxE6HsCAQOQoP6Oo0VE2yk+WdTNNsnK2t7K3YosBVZlknKT2Lp3qXdygVRo6/hx7QfTWPV2cDkkuPuyAZzOQOfK65A7ePTBTt0HNpUTblB3I7RlSXLMs9sewadXtfs8vKacp7d9qzD/z05Ek4tiwgEgs6gQXByuZbSYnP9EtfICLtuMG6jRhmrver1gFW9nYwtXDXsxjbuuGcgyzLJBclsTN9IWU0Z3i7eTAmfQmxgbLP1jJoirTiNZ7Y9w4HCA4r5FakriAqIYtHERUT4OWYG07rT6yjWFZvG51drCa3Vk2aVbu53802dvTWHw+PCC2HJy6ZxxZbNBP5jnm2Lw+LBzQ+qz/6uT2wGfS3JRQcb/N00RWphKikFKcQGxdq79XMSoewIBA6Atk5Lcn6yadynthafPDWlsvlm21J9HWvUnp5ooqLQJhvP27cAfCplSj0kkmz8Qu3ptLdiklacxm1rbqO8przR4/UuiM8u/swhFR7rwOQ5ZeWUZ2qoqzbH5rifdx6awYM7e2sOh0v//jiHh1Obboy3qd6fiL601LYHEpUaBk2F1O+N45pyyNjFxkL7ykJsSN8glB0bEW4sgcABSMpPotZQaxqP0eoaxOu4j7JP2QHwaCJuJ62mmLKaslbstOdQr5g09SRtb2xEvQuiKUWnHkd1QZwoOcGeXHM2YGhdHedXaxv0wfLv4d3NbUWSJGU2ml5P5TY7Kh5bx+0cX2/3Z/Jc/wzbg7DsCAQOQAMXVrWW6gKNYs7WTCxL3MeOpXD5/0zjqNMy288WV07OT+b83ufbv9kegD2KyUMbH+LeuHupqquiuq6aqtoqquqqGvybX53P8ZLjNl3fEV0Q1lada8orqClyprrA1TTnHBaG55Qpnb01h8Xzwgsp/vxz07hi8xa8L7nEtsWDpirHaevwHm1f0Le3y7nTpqOtdKllZ9myZcTGxuLt7Y23tzfjx4/njz/+MB2XZZmFCxcSFhaGm5sbkydP5sAB5VOYTqfjgQceIDAwEA8PD6644goyMzM7+60IBG3COjh5dJUyOFkdFIhznz7Wy1rEfeRIRZdmRdxO1rlbmTW5INnm2Ij08nSe3PokL+14idf3vM77Se/zyYFP+OboN/x64lc2ZGxgR/YOmxWdejakb2jN1jsEbZ2Wn4//bBqrZZlryispPtYwVudcTTdvDPexY5A05oeSir//RjYYmllhgVcohMSYx9lJTAkcYdf1p4ZPbVlIAHSxstOnTx9efvll9uzZw549e5g6dSpXXnmlSaF59dVXefPNN3n33XfZvXs3oaGhzJgxg/Jy89PYggULWL16NV999RVbt26loqKCyy67DP3ZoEyBwNGpqq0ipSDFNA6vrcWvSMJQZ9H8c+SoVgXLqjw8cIuONo37FIJvxdl6O+dwRtbG9I1dvQWHckGsPb1WsZ9JVdUEVMqUnbZIN9do8L322q7YnsOicnXF47zzTGN9URGZDz5E3htvUp2Y2LKr0iorK7Y4m6iAKJuuHR0QTUxgTMuCAqCLlZ3LL7+cSy65hMGDBzN48GD+85//4OnpyY4dO5BlmaVLl/L0009zzTXXEB0dzaeffkpVVRVffvklAKWlpaxYsYI33niD6dOnEx8fz8qVK0lJSWHdunUtXF0gcAwS8xOpM9SZxmOqdVS1oh9WU1j3yaq37qSUnlBc91yiIxQNCfuU0RJdSbvvwR4s0+Pf2POG4tjsxtLNL78cta9vJ+/S8XEdNlQxrli3jsLlyzl1w42cmj0H3bFjTS+2ituRjm9g0cRFeLl4NXtNLxcvXpr4UqsegM5VHCZmR6/X8+2331JZWcn48eM5efIkOTk5zJw50yTj6urKpEmTSEhI4J577mHv3r3U1tYqZMLCwoiOjiYhIYGLLrqo0WvpdDp0OnMdg7Iyx3nCEpx7WLuwxmq1VDcoJmh/vE497mPHUvjhh6Zx1GmZhOFQJdeSVpLGUP+hzazumdgb6zAhbAKXD7ocDycP3J3dcXdyb/DvwaKD3PK77b2i1p5ey4KNC/j32H8T6hHa8oJ2pKksNABng0xQjZ7iNGVW0bnYB6sldMeOUfz5yiaPa1NTOXXzLfT/YiWukZENBfqOAxdPqO9sfnw9EVd/wGcXf9bk/4+3izefXvypQ2bzOTJdno2VkpKCp6cnrq6u/POf/2T16tUMHz6cnJwcAEJCQhTyISEhpmM5OTm4uLjg5+fXpExjLFmyBB8fH9Orb9++7fyuBALbaVhMUKuw7EhubmiGtl4hcR8ZD07m5xrLPlmW7SnOJaaE2xdke9+I+7hs4GVMCZ/CuF7jiAmKYZDvIHp59sLH1QdntTOxgbE2uyDqWZ++nit+vIJPD3yqyMbrSFrKQqtVSbxXHqxMNx87Fs0QkW5uiSzLZD35FIaKimblDGVlZD31dOMuLScXGGBRnLCqELITifCLYNWlq/jiki+4bfhtOKnMn9/Kmkp8XM/tgo6tocuVnSFDhpCYmMiOHTu49957uf322zl48KDpuLWZTpblFk13Lck8+eSTlJaWml4ZGRltexMCQSupqq3iQIH5ptO/phbfMqirsmj+GRur6MNjLyp3d9xizL79sCLwKz8bt5NjX12PnkJsYCx9vWx7yLE1NkKSJJtcECqrr93qumpe3/M6N/x6Q4crn7ZmoU3ar7wx+4l08wZok5LQpqbaJpuSYqp31YAmuqBLkkRsUCyPj3mc2YNnmw7r0bM6bXWr9nwu0+XKjouLCxEREYwePZolS5YQFxfH22+/TWio0axrbaHJy8szWXtCQ0OpqamhuLi4SZnGcHV1NWWA1b8Egq5gX94+6mRz3MxYK6sO2NcPqyma6pOVmLu3zefujpTVlLV4wwf7YyMi/CL4bOxCogyNZyxFG9SsmrCY+XHzcVYpFdijxUe59Y9bWZiwkFJdqU3XsxdbstD65coMt3j+cwrrhddUkfVjTfl6+7Lpytetb/zAoMZbR1gyZ/Acxfi7o9+hN4gkHHvocmXHGlmW0el0DBgwgNDQUNauXWs6VlNTw+bNm5kwYQIAo0aNwtnZWSGTnZ1NamqqSUYgcGRs6oc1clSbr9NUn6wz2kLyq/LbfP7uxtJ9S1sMEI4OiLa/0nHeISK+vZtVp0/yRVYOc0tKmV1WztySUr7IyuHL0ycZ/v293Bs2mdVXrmZ8r/ENTvH9se+54scr+Pn4z+1SeFCWZY4WH+V/Kf/j0U2Ptig/a48yddrvxhuRnBwmvNNh0NsZ69mkvP8ACLD4G8vcDdUlCpEIvwhGBpvj9rIrs9l6Zqtd1z/X6dK/4KeeeopZs2bRt29fysvL+eqrr9i0aRNr1qxBkiQWLFjA4sWLiYyMJDIyksWLF+Pu7s5NNxn7svj4+DB37lweffRRAgIC8Pf357HHHiMmJobp06e3cHWBoOtpUF+nWktZgb95QpJwGxHX5uu4xceDszPUGuNCLOvtJOUnMb3fufN52ZOzp0Fn7+eH3k5m5g7KaivwdvZk6pDriBk+B0llx/OgLMOP94K2FAmI1dUQq6tpKKcthR/n02/eBj6Y8QFrTq3h1d2vUlBdYBIp0hbx9NanWX1sNc+e9ywDfQfa1cOruq6aXdm72JK5hS1ntpBT2XQMoyVeVTLnHzD/beid1fhed53tv4NzCLWdHoFm5QdNg8KzlbplPZzcDMOvVIhcP+R69uXtM42/PvI1k/pOQmAbXars5Obmcuutt5KdnY2Pjw+xsbGsWbOGGTNmAPDEE09QXV3N/PnzKS4uZty4cfz11194eZl94m+99RZOTk7MmTOH6upqpk2bxieffIJaFL4SODgVNRUcLDTHpw2sqcVPJ5NfYnZvuA4ejNqr+RgQW1C5ueEWG0v1XqPbqlcx+JfJFHlLJOYlnjPKjk6v44XtLyjmbtC7ct0fLykFU9ZC2P/BVcsgeFjLJ67TwaFfIWu/bRvJ2gdn9iL1Gc2sAbM4v/f5vLP/Hb46/BUyZmVjT+4erv3lWq4cdCUHCw9yqOiQ4jSWPbzcnN2Myk3mFnbn7G6xc3ZjTE2ScbHwjmRPjCTaKgFEYMRr2lQKly+3XX76tKYPRkyHXR+Yx2nrGig70/tNx3+3P0XaIgC2ntnKmYoz9Pbsbde+z1Uk2dEatHQBZWVl+Pj4UFpaKuJ3BJ3Glswt3Lf+PtP4+rJyFhyoImNTgGnO76YbCX3uuXa5Xv5//0vB+8tM43cuV/F3tIq4gGhWXraqXa7h6Ly7/10+SDbfVIL1Bn7KOINnU1+DGh+4608IGmrsUF18EopOGv8tPgVFp4z/lp0B7PwqPf9hmL5QMXWg4AAv7nhRoQTbggoVBmys3NvUOQwy7y7TE2jhban9+BVix1/RpvP2VGRZ5tTsOTYFKWtiYuj/zddNx37VVMIrA6BeQfXuDQ8fACv5t/a+xUepH5nGd8fczUMjH2r1e+gJ2Hr/driYHYHgXKFBfZ3qRurr2NnpvDncreJ2os7G7RwsOtwqK0B3I604jRWpKxRzzxQUNq3ogNHltHwKLAmHVwfA8qnw/VzYsAj2r4TTW6EsE7sVHWgQlwEQFRjFl5d8yZNjn8TD2aPhmiZoTtFxkpwYFzqOx0c/zs9X/tRk8PSYo7JC0TndVyJm3GU27+FcQ5IkwpYsRtXCA7LK25uwxf9pPsjdxQP6WcSZlp2B/CMNxK4bfJ2ieOUPx36gVt85JQu6O0LZEQi6iAbxOtr2rZxsjduIEYoU9vq4nVq5jkOFh5pa1iMwyAZe2P6ComL0jMoqplRVt7y4tho6orWD3LiColapuWnYTfx81c+cF3peozItEaAJ4KqIq3hz8pv8fcPf/O+i/3Fb1G0MKC9kUVYmXvqG1754r3Ju4IASpKx9DeQEZlwjI+n/xUo0Fi1ZLJFcXem38vPGCwpa0yAFvWFWVl+vvkzobVaKirRFrE9vIstLoEAoOwJBF1BeU66Iv4iQnfCrM1BdaFZGnEJDcQoLa7drqjQa3OLMwc6hJRBQejYFvYcXF/zmyDck5ieaxl6SM08WFrXPyT1DoO95EDHDvnX7V8K6F6CmqtHDwe7BRAXaV6QwPjiery79ig1zNvDSxJeY0W8Gni6eZoEjvxFRW8tn2blEWVSRD8+TiUq3OJG7nsjQSjj8q13X7whkWWZfejGvrDnMU6tTeGXNYfalF7dLplp74BoZSf9vv6H/118RMG8eKj9f0zFZp4M6G1uyWLWOaEzZgYZp6F8f+dqe7Z6ziHxCgaAL2Ju7F4PFk/3YinK0Jc7Iiuaf8e3e+8Z93Diq9uwxjaPSZbbESApFoKeRU5nD0n1LFXMPuw0iSG9Hl3JXb2Npf7/+xlRhvwHGn/36GV0QYMzGWj7F9iBlWQ9b34TU7+CS12Fww/Y29vbwivCNaF5BKjBm/ETU1vLlmVwOlLuTnu1O4CknLJ99gyIqkVQ06mrrTI7mlvPYt0kkZyrrDi3bdJzYPj68PjuOwSFtD+BvK5Ik4RYXh1tcHOoAf/JefsV0rPSnn9EMH97ySYKGGmN1ys4Yx6cTjIqwi7tC7MI+FxLiHkJuVS5gDGI/UXKCgb4D2+399ESEZUcg6AIa1NepqmhQX6c943XqaSpuJzFvv8M8Kbc3S3YuobK20jQeGTySa31tuPlYMmYu3PIdXPo6jL8Phl4CIcPNig4Yg0mvWgYaH2QZqgucyUvyInu3D3lJXlQXONPor7gkHb6cA1/dDKWZikP29vBqVF6W4dha+OQyOPwLALpSJ06vDcTpd18G7nfBu9jyViDjFnQ2Zd7N167rtydHc8u5bllCA0WnnuTMUq5blsDR3JaLQ3YmPpdeChYlC0p/+w3ZFuuOJCldWXodnN7WQMxJ5cR1g5XlAL45+k2r93uuIJQdgaALsFR2JM7G61gFJ7uPan9lx21EHJKL+Tr1fbIKtUVkVmQ2tazbsu70OjZkmCvdOquceX7C86iGmgNvbVJMhtoYqBs8DN2U5ZzaFM6pdUEUHvKi5LgHhYe8OLUuiFObwtFN/hCir2249vCv8O5YSHgHzgad2tvDa2q4RaXjuhpIXAXLJsIX18GpvwGjonNqXSDaIpcmziKRucUfXamT7e+7nZFlmce+TaJM27ySUKat4/FvkxxKUXcKCsJj4kTTWF9QQOX27bYtttGVdU3kNaglc6D5z2k/U11nQ/zZOYxQdgSCTqZUV8rhosOm8WAnH3z0BoVlR+Xujuvg9m+8qHJ1xW3ECNM4uBSCSnpm3E5ZTRmLdy5WzM2LncdAn4HQZzT4DzLe+NcGNq6YrA003vDDRkJv26pY644d49QDz6HNbfwmrc2t49QTb6KLexJuXQ3+g5QCtZXw1zPwwSRI32lXc1FTDy9tmVFh+u8I+PGfkGduDyHLkLXTF0Nt81/9hloVWft6I4e1v8JtC/szSpq06FiTlFlKYkZJx27ITnyuUKbrl/70s20LB0wCCyWmKWUn2D1YodiW15az5uQau/d5LiGUHYGgk9mbu1dROG5MnURtpVrRZdptRFyHlehvqk9WUn5Sh1yvq3h779vkV5tbYQzyGcTd0XcbB7VV6HIrm7VwaItcOLU+CF3cvxvUO2kMUxfsFtoImLpgD5wC9ybA5CdB7aoUyjsAH81E+uVBFo16vMXmol4uXrw04kGkdQvhrWijwlQf+2H5nqpDmrHoWMnm1qJNSbFJtr1ZezDXLvm/7JTvaLymT0Plbo61KV+3Dn1FZTMrzuLmC33GmMeFacY6To1g2RwURKByS4gAZYGgk2kQr1OS2zBepx36YTWFx9ixFFiMo9JlNsf2LMvOvtx9DeIYnp/wPM5qY7abvHEJWRvrMNQ2f+M31EhkvvgOIc94I1dVYaisNL309T9XGP+tzc62uwu2W1wcTP43xMyG3x+D41bNJfd9RsTh3/gs/gaeyfydA6qGzR+j9SpeMvQi4qPLwdBEzZXAwTDhQcrWZwMf27RHMDavtMzg6yxKq+2rHWOvfEejcnPDa+ZMSn/8EQBZq6V87Vp8r76q5cUR0yFjh3mctt4YM2bFuF7j6Ofdj9NlpwE4UHiAAwUH7M7gO1cQyo5A0Mnsytll+llCYlRpHlUFysDS9qyvY40mLg7J1dWYFsvZIGVZ5ljJMSpqKpSpyt2QGn0NC7cvVMxdP+R64oPP/k5zUtD+uhxtkX/DxY2d7/hxMu68q513aaVIBAyCW36AAz/AmqegwqKXVVUhEdveYxWQ4urCBnc3ylQqvA0GplZVE6OrQeJU4xcJHw8TH0IeOJ3yjRsp/fGDxuWawN5ml+2Fj5tzy0JtkO8MfK68wqTsAJT98rONys402LjIPG5C2VFJKmYPns3re143zX1z9BteCHyhgaxAuLEEgk6lRFvC0eKjpvFQjzB8DLKycrJKhSa2456mVS4uxsagZwkqg6BSY+G9lIKucVu0J/9L+R8nS0+axsFuweaS+gY9/LKA8syuvzlWbt9Oba6F+0WSjIHL9++Ccf/EmPttcRhjc9EFxaU8V1jMguJSYnU1NHSwScbA4rlrMdz8E8WJ5Zy47HLOPPgQ+sJCu/Zob7PL9mLG8BC75GfaKd8ZuI8di1OIeV+V23co/7+botcIcDe3jOHYX/Dzg7BuIWTsxjJy/spBV+KiMn93/HHyD7vLFZwrCGVHIOhE9uTuUYzHqH3Q10jGQNizaIYORe1pe6uA1uBhFbcTXZ+C3s3r7ZwoOcHyFGVzxqfOe8oc87LnIzizB31N13/1aVNTSZs6jYz77qdiyxZk/VkXlcYHZr0C8zZC4BD7Tjr0Urh/D/pZ71PweyJp06aT8/zz1Jw+3ao9Ntu8sgOJ7+tLbB8fm2QHBnkwoq9vx26oFUhqNd6XXWqekGXKfrWhSKNKpQyIN9TCvk9h61uwYrqxllOesSCpr8aXiwdcbBKtrqvml+O/tNdb6FEIN5ZA0AnIskxyQTIfJn+omB9dXU11oQtYPJ+7jez4DBjrejvD02U2xkFSXvcNUjbIBhZuX6hoCTEtfBrTws/esMuyYf2LAKhd7Gua6TZyJF7Tp6Py8Dj7ckfl4YHa09M0pzt+nPTb77Bv03o9FevXU7F+Pc5hYfjOvg6fa67FOSQYwkbAkFlQYOyRJMugLXSm/IwGfY0KtYsBr95aNAG1pvjpWnUfiv73PSXffIOhqvHKzDg7Q23LMS6amBg0sbH2vZ92QpIkllwdw6XvbG1RNrdUy4mCSgYFOZ771eeKKylaYW7cWfrTzwTMbeiSUpB3CE41876z9sNHFxkb1AYPY/bg2fx83Jzt9c2Rb7hp6E3tXpC0uyOUHYGgg0krTuOZbc9woPBAg2PvV59kUaFGMdeR8Tr1uMXEIGk0yFotYI7bScpPwiAbUEldb/mwl++Ofsf+PHP1Yk9nT54a95RZYM2/QWc08Xv11lJ4yPbKuyH/eqLFQF11QACa6Gibg5Stqc3KIv/t/5L/7nt4TpmM3/XX41FVgoSxNk7WTt8GmVSFh7zQ+NcQMKycijNulH7zKxgarznjOnQoAXPn4hoxiNO339Fs1phNzSs7mPwK25rTVtboueuT3fw4fyJ+HrZlmnUWmiGDcR06FN1hY6kJ3dGjaA8fRjN0aOMLZBl+vBdqm1BU69GWwo/zYd4G4oLiGOI3hCPFRqX4ROkJ9ubuZXTo6PZ8K92e7veNJhB0I9KK07htzW2NKjoAh53gYKnSZdUZlh3JxUWhVAWWQ0gJVNRWcLzEjjYKDkJeVR5v7X1LMffwqIcJdg82Do7+CQd/NB3TBIFr5ACbzm2rhcOeLth9lr2P/+23o/ZpxFWj11Oxbj0Z8/7B8Ve2krPXu8UU+TPbAig95d6oouM+/jz6/u9/DFj9Az6XX4Zm2LBmm1dqYmLo/8VK25pXdiBrUnMU4xnDQrhpXDj3Th7EG3PicHM2375OF1Zxz8q96OoaZqt1NXbV3MncY3u7kax9cGYvkiQxZ4iyX9Y3R0RFZWuEsiMQdBCyLPPMtmcor2m6nL1aLzMg2zx2DgvDOTS0E3YH7mPHKcZR3ThuZ8nOJVTUVpjG8cHx5pL6NZXw22MKeemCBbhGtRwEbq+Fo6Uu2PWKhNeUKYQ8+W8itmwm7LVXcRvdeKmB2qIqio95tlgEsOHGVXhfMov+331Hv48/xvP8iYr3YN280vf66wmYN4/+X39F/2++7nJFp05vUNTOcXFS8dYNI1h8dQz/ungo147swzs3jlSUP9p1soinfkh1qGrKgDFux6J9RNmvv5rjs6w58pt9Jz/bqPXSgZfi7mSu67M2fS2F1fYFo/d0hBtLIOggkguSm7To1NM/F1wtiu12hlWnngZ9stJlNoww1tuxLljmyKxPX8+6dHOlWSeVE8+Pf97sitv0MpRatPT2H0hN+GzKfmv+PWpiYghb/B+7b/z1ioQ2OZnydevRl5Wh9vbGa/o0NLGxCqVD5eqKz+WX43P55eiOH6fkm28o+fEnDKW2VQ9ugLMzfrOvw//OO3Hp27dZUcvmlY7GrpNFFFXWmMYXRgbh6aq8XU0fHsLTlwxj0W+HTHPf78tkYJAH902J6LS9toRzcDAe48dTuc3Y56ouP5/KHTvwtGgpYcLexqtn5T2cPbh80OWmwoJ1hjpWp63m7pi727DznkWrlJ2pU6fyww8/4Ovrq5gvKyvjqquuYsOGDY0vFAjOITamb2xRZmim8im0I/phNYVbTDSSxhVZq6y34+iVlOuDvTemb6RQW8hfp/5SHJ8XM49BvmfbMOSkwPb3lCe49E1yX39bEaTrccEFaIYObVYxsYfWKBKugwYR8uSTBD3yCOV//knx199QvXevXdf1u+F6Qp9+2t7tOhx/WLmwZkU3bu2ce/4AjudXsmqXWZl97c8j9A/w4NLYXh26R3vwufIKk7IDUPbzz40rO/Y2XrWQnz14tqKK8ndHv+Ou6Lu6ZfxdR9AqZWfTpk3U1NQ0mNdqtfz9999t3pRA0BOwpd7FECtlpzMtO5KzM+4jR1GZkACAfwWEFsNp6TRF2iL8NbYV3etMmgv2Bujt2dv8NHu2pg6yhcsg9noqclyo2GhWRCU3N3otegnnEMeo1aJydcXniivwueIKMhc8TPka23seyTWOVUm4NRgMMn8eMCs7TiqJ6cMa/7+RJIkXr4wio6iKrWnmuuCPfJNIbz83h0lJ95o+HcndHflshlzZ2nWEPl+laCkBwJBLjSnmtmLRqHWI/xBGBI0wuaHPVJxh25ltXNDngrZuv0dgl8qXnJxMcnIyAAcPHjSNk5OT2b9/PytWrKB3794dslGBoLvh7dJCQTZZVig7tW4uuEZ0rvnd/bzzFOP6uB1HTEFvKdgboFhbTHrZ2af8szV1TGh8kae+QN7LLyvWBP5jnsMoOta05IqypquKALYn+9KLySs3Z2JNiAjEx73pIpDOahXv3TySiGBz6rmuzsDdn+7hTIljdAJXubvjPcPc0VyuqqJ8XSNNPvuMhjAbszEbaVArApWbxi5lZ8SIEcTHxyNJElOnTmXEiBGm16hRo1i0aBHPPfdcR+1VIOhWTAmf0uzxkBLws+gNqI4djqRWNylvjSzL7Esv5pU1h3lqdQqvrDnMvvRiuwI0PcaOUYyj0h0zSNmWYG+Aqroqnt32LHJplqmmjomZL1H82wZ0x9JMU85hYfjfeWdHbLld8Jo2tWUhS/kuKgLYntjqwrLEx82Zj24fg79F6nlBhY65n+ymQtd4B/rOxtuWrCxJgquWGQtLNofGB656v0GD2pn9Z+Ljal675cwWsiuyrVefk9il7Jw8eZLjx48jyzK7du3i5MmTpteZM2coKyvjrrvav4eMQNAdiQ2MJSqg6aZ81i6s4HEX2nzuo7nlXPneNq55P4Flm47z5c50lm06zjXvJ3Dle9s4mtu8UlCPJioKlcZ8g6iP23G0pqC2BHvXk1qYSsofD5pq6gAQPgF9/8so+O87Ctngxx9DpdHgqGji4prM7Gog24VFANsLWZYVKecqyfZWEOEB7nx46yhc1Obb2uGcch5ctR99E7WHOhOP887DKSjINK7cvp3avLyGgsHDjAUDm7LwSGq4ZbVRzgpXtStXDbrKNDbIBr479l1bt94jsEvZ6devH/3798dgMDB69Gj69etnevXq1Qu1HU+lAkFPR5IkFk1cZG5VYIW1suNuY7zO0dxyrluWQHJm4xk7yZmlXLcswSaFR3J2xi12uGnsVwm9ioyWnTf3vElSfpJDpPLaEuxtyYZcc7NVVM5w2VvkL/s/9BZZTm6jR+F18cWNrHYc7Knd09VFANuDlDOlCtfT2AH+BHi62rx+dH9/Xr1OqfBtOJzHot8OttseW4uxfYQ5xgaDgbJfm0g1Dx5mbBdy93o4/2Hw7Wc+Jushu+laPLOHKLMMfzj2A7WG7h/L1VZaHaZ99OhRPvzwQxYtWsSLL76oeAkEAiMRfhF8evGnqKWGDwKKTCwnNW6xMS2eT5ZlHvs2iTJt86b5Mm0dj39rm6Licb7S3RZ9WsYgG/j4wMfc8vst3PjbjaQVpzWxunOwt7lhmUVdE85fgK7cmeIvvzTPSRIhTz7ZLZQDW2v3dHVtnPagoQvL/oyqq+J78+BUZezbx9tO8fn2U23ZWrvgc6WVK+vnZgoMSpIxhmf6QqNry5KdHygaglrSz7sf43uNN40LqgvsfljoibQqG2v58uXce++9BAYGEhoaqvjCkCRJxO0IBBb4afzQW2QEhbiHcPXp0/QtsGj+OWx4w8yMRtifUdKkRceapMxSEjNKiA/3a1aucHgfxXh4usxaCyPTgcID3LbmNj67+DMi/LqmfkmLwd7W8oazva/8B8IFj5I7/yGwKOTmc83VuEU17WJ0NOyp3dNdsXZhAVwU1boCmw/PGMyJgkp+TTbHqyz85SDhAR5MGhzUzMqORTN0KK6DB6M7ehQA3eHDaI8cRTNkcPML+02AkBjITTGOC47C8Q0Q0XiM1pwhc9ievd00/uboN8zsP7Nd3kN3pVWWnUWLFvGf//yHnJwcEhMT2b9/v+m1b9++9t6jQNCtOVp8VDGe1WsCtx1VKiy29sNaa1FV1hb+akFelmWeL/yUKotOBFHpcoOnxvKacmPgbxe5tFoK9rZmatVZV8ilb1KRsItKi5IYKg8PghcsaMfddQ71tXuCH32EXi8sJPjRR3CLi+sRig7AkdxyThaYI/ZH9fMj1Kd18VSSJPH67Djiw31Nc3qDzP1f7LM5nq2jsLbulP3SjHWnHkmC8/6pnNv5QZPik/pOItgt2CyavZNTpafs2WaPo1XKTnFxMbNnd58KqwJBV3Ks+JhiHGlQU52v7HPkNrLxdgHWlFbb53tvST65IJnUkoMc7mu+YfpWQu9GKs2nFqaSUpBi1/Xbizq97Rk10TodMboaiL0eOfx8cl9+RXE84J/3KAJFBY7BHyn2Z2E1h8ZZzYe3jqa3r5tprlxXx50f72Lj4bw2ZTG2Be/LLlNkUZX+0kz7CEuirwP3APP42J9Q2HgfO2eVM9cMvkYx983RczsNvVXKzuzZs/nrr79aFhQIBA2VnaoyqgqUyo6tlh0ft6brjbRGvt6Xf6Cf0jpQX2/Hmg3pnV8dvaC6gMe3PG6TrJfewEv5RUgaX5j5H4q//JKakydNx5379sX/9ts7aKeCttBeLixLgrxc+eiOMYpWE2dKtNz5ye42ZTG2BeeQEDzGm+tb1eXmUrVrVzMr6hdqYJRVmYRdHzYpfm3ktYpYwZ/SfkJbp7V7vz2FVik7ERERPPvss9xxxx288cYb/Pe//1W8BAKBmWMlZmVHLakZkHtK0cHauXcvmy0NM2xMw62npbTd+sDfA+FKZeeyXQZu3KQn8ozSpWVvoHBb0Rv0/HvLv8mvzjfNOUmNhxpG63R8lp1LRG0tzHyJulon8t97XyET/MTjqFwa7x4u6DqO51dwxELRiOntQ1//lmPYbGFIqBfv3hRPS84+e7IY24pNNXcaY8xcUFn8/e//ArSNfyZDPUKZ1GeSaVxWU8afp/60e689hVYFKH/44Yd4enqyefNmNm/erDgmSRIPPvhgu2xOIOju6A16jpeYTc3h3uEYEg4i681fve6jxza2tFHi+/oS28fHpiDluD4+LZbLrw/8rVXJ6CVQn9VrQkvg6u0yV2/XkxYK71+mJjNIsjtQuK28n/Q+O3N2msZqSc2Ki1bgpHJiQ/oGymrK8D6xhanpScToaow3tPAJMOIW8l96CUOZ+UbgPm4cXtOnN7yIoMuxtupc3EYXljWTBgfRy1dDVknzlo36LMYf75vYobFQ3jNmkPPCi8jVxtiy8r/+wvD8c6jc3FpYGAbDr4TU743jmnJI/LJhPM9Z5gyZw4YMszX2m6PfcGXEle3yHrobrbLsWBYTtH6dOHGivfcoEHRbMsoz0OnNpe8jvcKpTlcqKvb0w6oPvNQ4N//R9dY48drsloNXp4RPoU++zAtfGkyKjjUROfDiSj198mWmhttX0bct/J35Nx8mK830D496mJHB8cRqdSwoLuG540ksOLab2HpF52xNHe2xNEq+tohRUKkIeap7pJqfi/yRqqzy29Z4HWv2Z5S0qOjUU5/F2JGoPDzwmmbOpDJUVVG+br1ti8dZKTa7PoD67EMrxoeNp4+nOdsyOT+Zw0WH7d5vT0C0QxUIOhBLFxZApMqDqvzWxevUMzjEi/MGBjR5PDLYk+/uncDgkMaLGVoSExDDo2uc8WzhPuCphfm/6SnVlti119aSVZHFk1ufVMxNC5/GbYFjYPkUWDHd2DAxzaq/UPwtyEFDyH15ieIG4Dt7NpohQzpj6wI7ySiqIvWM2QI3JMSLgUGezaywn/bOYmwPGtTcsSUrC6DPGGNfrHqKTkDa2kZFVZKqQZFBy87o5xKtcmO11BLio48+atVmBIKehnUxvkidjmqL4GSVhwaXQYPsPu+x3Iomj10cHWqTogOgS0qmd6ZtT7wR2fDCqgX43PkJsUEd15agRl/DY5sfo1RntoD19erLS4NvRfr4YtA248I78AMVNfFUbd9hmlJ5eRH0kHCtOyod7cKC9s9ibA88xo9HHRiIvsDYrb1yWwJ1BQU4BQY2v1CSjNad1f8wz+1YBoMvalT8qoireHf/u6Yqyj8e+5E6Qx0BmgCmhE8hNrBn1GlqiVannlu+8vLy2LBhAz/88AMlJSXtvEWBoPtibdkZdCIDvc6cIeEeG4Wksu9jmFOqVZTUHxjkoTi+8Ugj/XaaoPynL+y6duwhLfPXz+dESce5q1/f87oixd1F5cKbk97A67dHm1d0AENlKbmvvqaYC5w/Hyd//w7Zq6DtNHBhxbS/stPeWYztgeTkhM+ll5on9HrKfmuifYQ1UVeDp0XywYmNkNe4e8pf4895vSyyv+Q6fkz7kRWpKxymQnpn0CplZ/Xq1YrXr7/+yokTJ7jhhhs477zzWj6BQHCOYJl27ubkhk+q8kvFbdz5dp9zX3qxYnxJdC+GhpotOalnysgptc1ao884ZNe1PbVQqivlH2v/QU5lTssL7GTNyTWsOrxKMff0eU8ztKoCspruB1RP8VEPakvNNUtc+vfH/+ab2n2fgvYhp1TLvvQS03hAoAdDbLRK2kN7ZzG2Fw1cWbZmZTm5wGgrD8uuxosMphWnsTd3b5Onqq+Q3tMVnnaL2VGpVDz88MO89dZb7XVKgaBbo63Tkl6ebhoPcgum+qRSUXG3sZigJXtOKc8xqp8f04YFK+Zste6oXRoPbGyKirMFbXOrcrln7T2UtGMMz4nSEzyf8Lxi7spBV3J1xNVwpOUn3jqtioIDyhtl8L+eQBKp5g7LnwcaurA6wqVSn8VoC7ZkMbYXrsOG4RppbsGiPXgQXZqNSsfou4wB+fUkfQXVyu8GWZZ5ZtszVNVVNXuqrq6Q3hm0a4Dy8ePHqauzvdKpQNCTOV56HIN8VpmQZc7bd5DyMxbl7yUZ112PQp591pW9Vpad+HBfpg5VPomuP2SbsuM1cqBd194Xaf7KOFF6gvs23EdVbfNfpLZQVVvFo5seVXwpR/pF8vR5TxtvftUlLZ4jP9kLQ515fx4TJ+I5eXKb9yboODo6C6ue+ixGb03zYaqerrZlMbbnvlpdc8czGKKvNY9rq2Df5wqR5IJkDhQesOl0XVkhvTNolbLzyCOPKF4PP/wwN9xwA9dffz3XX399e+9RIOiW1Luw+uTLLP5Ez8zvXDHUWHQ/lyXSvzyD7o2LbVZ4tLV6Dpwxx61EBnvi6+7CiL6++HuYLRjb0grQ1rZcgl5z0W1o/GtsurbGv4Y7ZvxDMZecn8yjmx81BT+2BlmWeWnHS6SVmJ9oPZw9eHPSm7g5na074ubb7Dm0xU6UnLAoQqeSCHny3+dE4GV3pbBCx66TRaZxb183YnrbZn1pDYNDvPju3gnNWnguiAy0Obi/vfC5/HJl+4hff0VuIpW8AePuUY53LQeD+XNvb7fzrqiQ3lm0StmxbPy5f/9+kpOTAXjjjTdYunRpe+5PIOi2HCs+Rp98mRdX6oloIrxFW+TCqd816P43r0HzzcZIyiihzmCWG9XP2NFcrZKYPMRchbm6Vs/2E400uLJC6juGsMt6oXJu/stVUsmEXdaLy0Y/wBNjnlAc23pmK89te85sxbKT7459x68nflXMvTjhRfr79DdPDLmUppBlyN3nAxY1cv0un45rRNd0aBfYxl8Hc7H4U2ZWB7mwLBkc4sVP901k9fwJ3Dt5ENeO7I3K4pIbDudRXGmb8t9eOIeG4j5unGlcl51N1a7dti3uPRL6mtdSmg5HfjcN7a143tkV0juTVqWeb9xon7YoEJyLHMvaw/xf9S3WsDHUqsj6NZv+t+5B6jumWVlrF9bIs8oOwLShIfyw74xpvOFQHlOGKGN5GiBJuN69nP6lF5P1t5OijYUlMjKqiXeBJHHr8Fsp0hbxv5T/mY7/euJX/DR+PD76cbtuWAcKD7Bk5xLF3C3DbmFm/5lKwcBIY5l8Q0M3eXmmhqp8V9NY5SoR+K8XbN6DoGv4wyrlvCOysBpDkiTiw/2IDzd+dlSSxLd7MwHQ1Rn4ancG9062vxxEW/C5/HKqdpjLJZT+8jMe541rZoUF4+6BDHOVcXZ+AMMuB7C74nlnV0jvTNoUs5Ofn8/WrVvZtm0b+fn5LS8QCM4hag8da9KiY422yAXtn5+1KLfvdMPg5HouGByIk8Vj6obDebYFHAYPw/XRNfS/vQ/9Z+QTMKwc30GVuHhZuKYMKgre/S+c7T7+YPyDXBOp7Kr8+cHP+SjV9hpbpbpSHt2kdIHFBcXxyKhHGgqvf7FRRcegh7xE5Rd00D/vwsnfr4GswHEoraolIa3ANA7xdiW+b9f8n90xsb9i/Pn2U9TpW2elbC1eF81EcjUr7OV//oVBa2PTzmFXgFeYeXzqb8hJBYwV0u2hMyukdzatUnYqKyu566676NWrFxdeeCEXXHABYWFhzJ07l6qqtgcrCgTdnWJtMZFH7OswXL73eLPHZVlmr4Wy4+fuzMBAc40db40zYweY68mcKalWNFdsluBhSP/YhNu/1hB83930+uc19H1wFkhmZakkVYvu51cA49Pxs+c9y5S+yi/TpfuWsvrY6hYvV58lcqbCbInyc/Xj9Umv46y2qnGSvgP2rLBYC9UFzuQleXF6fSC1lWYDtUu/Pvjd/ZBt71nQZaw7lKtwx14UFYpK1TXxVVFhPorPTVaptlMqKFui9vRUto+oqKBig43xM2pnY4NQS3b+HwCxgbFEBUTZdJrogGhiAmNsu2Y3pNUByps3b+aXX36hpKSEkpISfvrpJzZv3syjjz7a3nsUCLodaSVpLbqvrNHXNP9xPFFQSXGV2Qoyqp9fA5fR1KFKt9WGw7YXGESSoM9omL4QLl+Ky63v4DvdokmpLJH/fx9DqVFBcVI58eqFrzIyWNnba+H2hS0GRn5y4BM2ZWwyXxqJly94mVAPK1dGXQ38YlZedKVOnNo2lFPrgig85NXA7eZ/591Izh1fEE7QNqyzsDqiarI93Dmhv2L8ybZTnb6HVtfcARh1J6jNliFSvoXKQiRJYtHERXi5NB90rZbUvDTxpR4d0N8qZef7779nxYoVzJo1C29vb7y9vbnkkktYvnw53333XXvvUSDodhwtPmqqSWMr6r7Dmj2+93TT8Tr1TBumTEHfYGMKelMEPvM6krP5C7D8tDPVKx4wjTVOGt6Z9g6D/Qab5gyygce3PN5kIbM9OXt4e9/birl74+5lQu8JDYW3vQ35xsqwulInTq0PQpvZdBBl3ptvojt2rMnjgq6nQlfHlmNmF5a/hwtj+3dthesZw0Po7WvuOL7rVBGpFlmPnYHHxImoA8w97yq2bqWusOUkA+PiAIi16IFVp4V9nwAQ4RfBZxd/1qyFRy/rya/u2aEorVJ2qqqqCAlpWGEyODhYuLEEAoyZWHsi7ft4eV11c7PHG8TrhDdUdgYEeihcW/vSiylqQ3aJc0gw/jdcp5jL+zER+cifprG3izf/N/3/6O3Z2zSn0+t4YP0DHC48TFJ+Ekv3LuXF7S+yZOcSFmxagF42p8dOCJvAP2KVKe0AFByDLa8CRtdV1k5fDDXNP3kaysrIeurpHl0crbuz4XAeNXXmmJiZw0NwUndtT2ontYpbx/dTzH2ScKpT9yA5OeF96SXmCb2est9+b3qBNQ26of8P9EZLcIRfBKsuXcUXl3zB3Oi5zB48m7GhYxXiS/ct7dGfm1b9hY0fP57nn38erUUAVXV1NS+88ALjx49vt80JBN2VYyXHOBYGaTZa5zVDI9DExTUrY2nZcVJJxDVR5dXSlWWQYfPRtll3Ah58HLVFDZ+qPFcqP3wUaswPNkHuQXww4wP8NeYn9PLacm787UZu+f0WVqSu4Nuj3/Ll4S8VDT5D3ENYcsES1CqL+kNg1G5+WQB6o6KmLXRuMlPMGm1KCtqz5TAEjscaB3Nh1XPDmL5onM23xJ8Tsyio0HXqHnyuuFIxLv3ZDldWaAz0s2g/U54Fh34xDSVJIjYolgWjFvDc+Od4b9p7hLibjRYHCw+y9nTj3dN7Aq1SdpYuXUpCQgJ9+vRh2rRpTJ8+nb59+7Jt2zbefvvtlk8gEPRgDLLB2GdGknj/MjVVmuatESpPD8Jee7NZf3lJVQ3H8sydzqN6+6BxVjcqO9WqdYSt1ZSbQu3lRcC99ynm8hKqkTcrG2728+7HsunL8HA2W5bq5OYrqi8YuUChIJnYvxJObzUNy3PsS4ktX7feLnlB51Bdo2fjYbO7xEvjxIRBLXT57iR83V24Ot5snazRG/hqV3ozK9ofTdRwXAaZ0961qanoTtjRdPc8K+vO2UDlRq/lpGH+iPmKuXf2v0NdI1mPPYFWKTsxMTEcO3aMJUuWMGLECGJjY3n55ZdJS0sjKsq2yG+BoKeSVZFlanuQGSSxZXafJmU1MTH0X7UK18jIZs+536JZIjTuwqpnTH9/vFzNGUqbj+ZT28ZUWr9bb8cpyHxNXbELZas+bNBpeXjAcJZOXoqEbYGOXxz6oqHpvCIP/npGMaUPGm3XfvVlPbc4Wndm89F8qi0qe88YFoKLU9e6sCy53SpQ+fMdp9v82bEHSZLwsWofkfX002Q/v5C8N96kOjGxeVfTkEvAJ9w8ztgJZ/Y1KX7FoCvo793fND5Vdoqf0n5q7fYdmlb9lS1ZsoRVq1Yxb9483njjDd58803uvvtuVq1axSuvvNLeexQIuhWWnc4BwiuVbRs0Q/oTMG8e/b/+iv7ffN2iogMNg5NH929a2XFWq7jQoppyubauQfNQe1G5uhL08OOKufxkd+SfH25Q+dnN2Q0Z23z/jfbjWfMkWDYY7T0K9SBlfEFLqL17bnG07oyjurDqGRrqzYRB5iDh3DJdg+KHHY3PZcpq4dr9iZR8/TWFy5dz6oYbOTV7TtNB+Co1jJ2nnNvZeDd0MGZUPjjyQcXc+0nvo62zM5W0G9AqZeeDDz5g6NChDeajoqL4v/9r2mwmEJwLHCtRfhH1PV2iGAc//AjBjz6CW5ztDQetlZ1RjWRiWTKtQQp62+uG+Fx5Ba4D+5vGtRVOFG9MhKRVCrk29eM5thZSLTI6VU5w+X/xOP8Cu87pNX1ay0KCTkVXp1e4VD1c1Fw4OKiZFV3DHQ3S0E926vUNVVWgbtxFDUbX1qmbb2la4Rl5Kzhb9IlL/R7Km/78Tw+frsjUyqvK46vDX9m9b0enVcpOTk4OvXr1ajAfFBREdnZ2IysEgnMHa8uOd4aysJ9mpI1l4M9SqzeQmFFiGvf2dSPEu/m89slDgi17C7Lenno7TSCp1QQ9prTuFKR6YfjtGagyN3RsdT+emkr41ap68oQHkEOiKP72W5vPp4mJQRMba9ceBB1PQloh5TpzPMiUocFNxp11JdOGhdDHz5yGvi+9hCSLz19HIssyWU8+Bfrmm/g2m3Xo5gdxN1gI18Lej5s8lyRJLBi1QDG3PGV5j+uT1Splpz4Y2Zpt27YRFhbWyAqB4NzBUtlxxQkKzVqHs5+r3S6Ww9nlijiHlqw6YKxdMtIirudEfiUnCyrtum5jeE6ZgtvIeNNYr1NTuF8H6543zbW6H8/GxcZGhvX4DYBJ/6L4s88o/+03m86l8vYmbPF/enRxtO6KdSHBWdENH5gdAbVK4vbx/RVznZWGrk1KQpuaaptsc1mHY626oe9eYSzQ2QTn9TqP83qdZxqX1ZTxSeonNu2ju9AqZefuu+9mwYIFfPzxx5w+fZrTp0/z0Ucf8fDDDzNv3ryWTyAQ9FBq9DWcLjttGo8r90XWmz9mmoH2f8HvPV2kGDcXr2NJm6opN4EkSQRbVUkvOuxJXcJKY1sHWtmPJysRdryvPHDZW1TuTyH3VWXWl2XhNUs0MTH0/2KlTTFQgs6lTm9grUULBlcnFZOHOJ4Lq545o/viZmF1+jU5i7zyjo9jKV9vY4uIevmmsg6Dh8JAi89hZR4caL6Ny0MjlW1WVh5aSUF1QRPS3Y9WKTtPPPEEc+fOZf78+QwcOJCBAwfywAMP8OCDD/Lkk0+29x4Fgm7DydKTinTr+Gyr4ORo+3vP7LXKxBrZTCaWJQ2Vnfbp9+M+ahSekyebxoY6FYUHPeHXh0Ffa38/Hr9h8MuDIFtkvcTdSK3HMM48/IjCpO8+ZgwRGzfQ/+uvCJg3D9/rr7c72FvQ+ew8WaRodTJpcBAeFhmDjoaPuzPXjjKnodfqZb7c2fFp6PZmETYrb11kcOeyBskElkQHRjOj3wzTuLqumg+Smg5u7m60StmRJIlXXnmF/Px8duzYQVJSEkVFRTz33HPtvT+BoFthHZw8MF3pOnIbM8nuc+49ZbbsuLuoGRrafJ+beoaGehHmY47t2XmiiHJtbTMrbCfo4YexDAoqTvOg9uQR2PG+zf14vFy8jP14dn0A2UnmA27+GCY/T+aDD6G3KJfvFBJC76VvoXJxwS0ujuBHH6HXCwvtDvYWdD4NXFgxjpWF1RjWrqyVO9IVlZ87Antd3M3KR840uoLrydoPmbubPd8D8Q+glswWre+OfkdGeYZde3JU2lTgwNPTkzFjxhAdHY2rRXt6geBcxTo4OTCzQjF2HdVI/6dmyCqpJqvUbD4f0dfX5tL6kiQpCgzWGWT+PtY+ZmnNkMGKeiCyQSI/xQs2vQwl6S3244kOiOaziz8jAmfY+B/lwYuXkPvmMkU8guTsTJ93/otTEy4sgeOiN8isSTVbFZ3VElOHNmw35GhEhnhxQaS54GFBhY7fUrI69Jpe06baJ99c1qFKBeOsYneaKTIIMMBnAFdFXGUa18l1vJf4nl17clQcp5qTQNADsFR2JFnG2aK3nrOvM05+trmg6tmXblVfx4bgZEumWd1U2lpN2ZKgBx9QdBgvPeWGNr8G/vg30Hg/nrnRc/niki/48tIvifAdBL89ArUW/fQGTqb4sIoSq+yrkOeexU1kWHVL9p4uVrRdmBgRiI9b9+hMb52G/vG2Ux3aP0oTF4cmOto2WVuyDkfcDC6e5vHBn6CseYXtn3H/xEVlbs3y+4nfOVJ0xKY9OTJC2REI2hFLN1ZEqQbqLIKT+9sfnGxdDLCxTufNMX5QgKLfz6YjeRgM7fNl7dy7N3433WQxI5Gf7A1HfoPDxuwp6348C0YtIDYo1uhySv0e0taZlztpqB5wD7mLFimu4ztnDn6zZyPonjTMwnJ8F1Y9U4YE0y/AXLMmObOU/R2Yhi5JEmFLFqNqwZ2l8vCwLetQ421UeOox1Bkzs5oh1COUm4aZP9cyMv/d/98W9+7oCGVHIGgnymrKyKk0V1sdl6t8etW0opWKpWVHkiDexuBk0zWd1ZwfYTbFF1bWkJRZYvc+miLgn/eg8jQ/OVZkaajKc4HfnwBdRdMLq4pgzb8VU3XxD5L5zKvItea4Ik1cLCHPPN1u+xV0LrIs86dFBWK1SmLG8O6j7KgaS0PfdqpDr+kaGUn/L1Y2a+FxDg/HJSLCthNau7K2vws/PQDrFkLG7kaDludGz8XT2fy53pK5hb25e227noPSpcrOkiVLGDNmDF5eXgQHB3PVVVdx5IjSXCbLMgsXLiQsLAw3NzcmT57MgQMHFDI6nY4HHniAwMBAPDw8uOKKK8jMzOzMtyIQGJt/WjD0jLJjsmaMfVWAq2rqOJBlzrYYHOzVKvO/dXxEe6Sg1+Pk50fA3LsUc3lJ3silmbC5mdYxa5+DSrOPTw6M4syqQ9TlmmM71AEB9Hn7bVQutnU7FzgeSZmlipizcQP88ffoXv+f143ug4eLOWj395Rscko7Ng3dNTKS/t9+Y8o69Ln6aiSNOdlAd+gQ5WvW2HaygEEQbhErWKeF/Z/B1rdgxXRYPgXyDimW+Gp8uTP6TsXc2/ve7lAXXkfTpcrO5s2bue+++9ixYwdr166lrq6OmTNnUllpzmB59dVXefPNN3n33XfZvXs3oaGhzJgxg/Jyc1XaBQsWsHr1ar766iu2bt1KRUUFl112GfoWqlAKBO2JdXByWGaVYqwZbZ+yk5xZit7C5WSvC6se6xT09ozbAfC//XbUgWbrUXWhCxVnNLD9Pcg90HDByb9h/+cWExJ5WeOo2mPx5KhW02fpWziHdh8rgKAhDbOwHLOQYHN4a5y5bpS5mW+dQeaLnaebWdE+SJJkyjoMW7KY4CeU1cvzXn8Dg07XxGpLwUOQk9T08az98NFFDRSeW4bdQoDGnBCwP28/WzK32PUeHIkuVXbWrFnDHXfcQVRUFHFxcXz88cekp6ezd6/xS0+WZZYuXcrTTz/NNddcQ3R0NJ9++ilVVVV8+eWXAJSWlrJixQreeOMNpk+fTnx8PCtXriQlJYV169Y1d3mBoF1RpJ3LMh55ZkXFycsJJwuFwBbs7YfVFKE+GqLCzDEAB7PLyC6tbtW5GkPl7k7g/HsVc3nJXsh6vbH2jsEiXbdWC78uUMiWOl1K0Q/Kp9SQfz2B+5gx7bZHQecjyzJrLFxYkgQXRTl+FlZjWHdD/3JnOtrazn2Y9pszB5eIQaZx7ZkzFH36WfOLZBl+vNfYiqU5tKXw43yFS8vd2Z174pQusKX7lqI3dE8jgkPF7JSWlgLg7+8PwMmTJ8nJyWHmzJkmGVdXVyZNmkRCQgIAe/fupba2ViETFhZGdHS0ScYanU5HWVmZ4iUQtBVLy05ICUi1FsHJA+z/km8vZQcaawzavtYdv9mzcQ4PN41rypwpPeUGGTuVVpy/X4dCs7tPWxtG9ndK64/35Zfjd+ut7bo/QedzKLuc04Vm6+bofn4EezXf081RGRjkqaj4XFhZw6/JndsHUnJyIuRfyji3wv/7P+ry85tYAWTuMVpubCFrH5xRxuVcF3kdvT3NxRXTStL4/eTvNu/ZkXAYZUeWZR555BHOP/98os8GZuXkGJ8KQkKUN4qQkBDTsZycHFxcXPCzSum1lLFmyZIl+Pj4mF59+/Zt77cjOMeQZVlh2RmZp6w7pRk+zK7zGQyyIjg5wMOF/hZZIfYydZhV3E47u7IkZ2eCHnpQMZef6oVBD/z5FPzxL/jmdtjyuum4XieRudUfWWs2xbsOHUqvF18QBQJ7AGusXFgXO2gvLFtpmIZ+stNjWDwvOB+PC83ucENVFfn/bSZT6ohtPeVMHP5VMXRWO3N//P2KufcS36NW3z7FSTsTh1F27r//fpKTk1m1alWDY9ZffLIst/hl2JzMk08+SWlpqemVkdEzKkQKuo7cqlzKa8xxZLHWbSLsjNc5UVBJiUV5/ZH9/NqkAMT29iHQ0xwYujWtgOqa9jVHe8+ahWb4cNO4rsqJ4mMeUFNhLGZ28EfAeHOQDXAmaRC1uebq0CofH/q8819Ubm4Iuj9/pCofNi/uRinnjXFhZBADAz1M4wNZZeyxsr52BiH/+heozQHTJd99j/bQocaFq0vsO3kj8pcMuITBfoNN4zMVZ/j26LcN5Bwdh1B2HnjgAX7++Wc2btxInz7mQLDQs8GJ1haavLw8k7UnNDSUmpoaiouLm5SxxtXVFW9vb8VLIGgL1sHJ/RpkYk2263zWzT/b4sICYwrtlCFmV5auzsD2E+3b5E9SqQh69BHFXMFBL/Q1DZW0/FQvKk9YBHBLEr1ffx0XYWXt1siy0SL57++TOZZnLj0Q29ub3r7dW4lVqSTumNhfMdfRaeiN4TpoEH433GCekGVyX36lcSuTm699J29EXiWpGjQJ/SD5A6pqqxrIOjJdquzIssz999/PDz/8wIYNGxgwYIDi+IABAwgNDWXt2rWmuZqaGjZv3syECcZUulGjRuHs7KyQyc7OJjU11SQjEHQ01sHJfrnmoFwnTzXOIcGNrGqa9ozXqWfasI7NygLwnDgR9/POM40NNSoKD3kqZMozNRQeVPbNClqwAM8Lzm/3/Qg6j6O55Vz53jaueT+Br3YrreV55TUczS1vYmX34ZqRffCyaGC65kAOWSXtF+xvK4H336coPFi1cycVGxrpmD7kUvtOPPSyRqcv6H0BI4NHmsZF2iI+P/h5o7KOSpcqO/fddx8rV67kyy+/xMvLi5ycHHJycqiuNv7xSJLEggULWLx4MatXryY1NZU77rgDd3d3bjpbudXHx4e5c+fy6KOPsn79evbv388tt9xCTEwM06dP78q3JziHsLTsBJaBWmcRnNwvqLElzWKp7DirJWJ6+7Rtg8D5kUE4q81Wlg2H8zok5iD4losV48IjHmTv9SZ7tw/Zu3w4s91Xcdzr/NEE/GNeu+9D0HkczS3numUJJGeWNno8p0zLdcsSur3C4+nqxOzRZuuj3iDz+Y6OT0O3xsnPr0EGZO6rryLX1CgF+4yGsHjbThoWD71HNXpIkqQG1p1PDnxCsbbz3XitpUuVnWXLllFaWsrkyZPp1auX6fX111+bZJ544gkWLFjA/PnzGT16NGfOnOGvv/7Cy8v8ZPjWW29x1VVXMWfOHCZOnIi7uzu//PILagu/pkDQkVgqO5FWcfH2BicXV9ZwPN+cKhrd2weNc9v/lj1dnThvoLluRnaplkPZ7X/zceMQXn0tnnYNKkqOeVJy3IOSEx7IevPXjot3Lb2uDBcByd0YWZZ57NskyrR1zcqVaet4/Nukbl2YDuC28f2w/HNdtavz09AB/G+6CZd+/Uzj2tPpFH3xpVJIkuCqZaCx4WGp73nQzOdwZMhIJvWZZBpX1FawIqX51hOORJe7sRp73XHHHSYZSZJYuHAh2dnZaLVaNm/ebMrWqkej0fDOO+9QWFhIVVUVv/zyi8iwEnQatYZaTpSeMI1H5Cg/VppRE+06n3Xzz1F2tohoDsu4HYANh3ObkGwD1SX49K+kPhi5aWSC48pQ00INEIFDsz+jpEmLjjVJmaUkdmBvqc6gf6AHUy0+RyVVtfyUeKbT9yG5uBD8rycUcwXvv0+dVfwqwcPgrj9btvDs+QgKjjUr8kD8A0iYFaJVh1cpWuQ4Mg4RoCwQdGfSy9KpNZgzpyKzlE+4mrFT7DqfdbzO6P7tp+w0iNtp53o7ALLGh4ID3kBL1hqJggNeyLY8dQoclrUH7VOY/7JT3hG5c6IyvrSju6E3heeUKcoYufJyCt55t6Fg8DCYtxHuXg/nPwyj7oSJD0OfsWYZvQ5+fkBZBNSKIf5DuHSgOQ6oxlDDsqRl7fJeOhqnlkUEAkFzKDKxZJngHD31zxFqdxVOveyrL2Kt7IxsR8tOvwAPBgV5mNxkiRklFFToCPR0bWGl7WgNQ9EW2db/SFvkgpZhdO88nXOb0mr7aq7YK++ITIwIICLYk7SzGWeHc8rZcaKI8YMCWljZvkiSRMiT/+bk1deYlJTir7/G76YbcbVuFCpJxhiePqPNc6WZ8N44Y3kIgPTtsGcFjG06hm7+iPmsObWGOoPxoW71sdVISKgkFd4u3kwJn0JsYKzDuaaFZUcgaCNHi4+afvYvBxetRXByeJBdH/pavUHRlbyvvxvB3u1bdXaaRYFBWYZNR5qpwNoKylOz7JNP7nwXgKD9sLc5bWua2ToakiQ1KDL4ScLJLtmLZsgQfK+91jyh15P7yqu2LfbpAzNeUM6tWwgl6U0u6evVl9mDZ5vGMjLfH/ueb49+y4rUFdzy+y3c+NuNDRojdzVC2REI2khaiflDPSBXacrWDBtsLd4sB7PK0NaazcjtGa9Tj3Vj0PaO29GX2Rf0bK+8wLGYaKc1Y+bw7tkfy5prRvbGW2N2jqw9mEtGUdfUngl66EFUHuaCh5V//03FFhubdo66C/pZxBXWVMAvCxR9sqyZ0W9Gs6c8UHiA29bc5lAKj1B2BII2YunGGmrVLkczyr5aTw3q6/T3b/W+mmJUPz/Fl/TfRwuoqWvaT28vajuLdNorL3AcKnR1vPHXEZvl4/r4MKKvb8dtqBNxd3HihrHmfnAGGR5YtZ+nVqfwyprD7Esv7rQ4HqfAQAL+qWzamfvKq8i1NrgMVSq44h1wsrAgH18PSQ27GYAxseiNPW+0eNrymnKe3fasw2TfCWVHIGgDVbVVZFZkmsbDc6wsO+Om2nW+vR2YiVWPs1rFJItsknJdHXtOFTWzwj68ptn3nr2mT2u3aws6jwpdHXd8tIv9GbZlYnlrnHhtdpzDxXK0hVvP66cIw0/MKOHLneks23Sca95P4Mr3tnVabSH/227Dube5aWfN8eMUf/ONbYsDBsGUp5Rza56E8oZW3+SCZA4UHmgw3xiphamkFKTYtocORig7AkEbsHRhAfTJNltI1BoVzn1sL4EgyzJ7T5mVHQ8XNUNCvZpZ0Xqsu6C3Z1aWJi4OjVV5iCZlY2LQxMa227UFnUOlro47P97VoDdUaBPxZXF9fPju3gkMDumYv+euorpWj1rVtPKWnFnaacUUVa6uBD/+mGKu4J130Zfapoxy3n3K9HRtCfz+WAOxjekb7drXhvRGKjt3AULZEQjagKULy7dCxq3K/MWnCfe36yk2q1RLTpnWNI4P92v2i7QtTBochOWpN7SjsiNJEmFLFivK2TeGytubsMX/6VFP+ucCRkVnN7tPKRWdR2cMZvuTU1k9fwL3Th7ETePCuXfyIFbPn8CP903scYpOfTHFOkPzbprOLKboddFFuI0yV0HWl5RQ8L6NqeFqJ7jiXVBZJGkf+hkO/qQQK6sps2tP9sp3FELZEQjagGVPrAHWLqyh9gUnd0Q/rKbw83BRnP9kQSUn8iuaWWEfrpGR9P9iZZMWHk1MDP2/WIlrZGS7XVPQ8VTV1HHnJ7vZZeX2fHj6YB6YFokkScSH+/Gvi4ey+OoY/nXxUOLD/XqkQuuIxRQlSSLk3/9WzBV98QW6kzZmioVGwwWPKud+ewyqzP/f3i72xdjZK99RCGVHIGgDlpadgdZtIuLH2XWufZ2o7ABMHarMimlP6w6cVXi+/Yb+X39FwLx5+F5/PQHz5tH/66/o/83XQtHpZlTV1HHXJ7vZdVKp6Dw0LZKHpp97/5eOWkzRLSYanyuvNE/U1ZH32uu2n+CCRyFoqHlcmQd/Pm0aTgm3r0jq1HD7Yvg6CqHsCAStRJZlZSaWtWXnPPsa0e45bb6JSBKMCPdt0/5aojO6oEuShFtcHMGPPkKvFxYS/OgjuMX1rCDVc4HqGj1zP9nDjhNKRefBqREsOAcVHXDsYopBjzyM5GYu1VmxYQOVO3bYttjJFa58DyQL9SDpS0hbB0BsYCxRAVE2nSo6IJqYwBib992RCGVHIGglhdpCinVma8xAi+BklauEc/8BjS1rlEpdnaIp55AQL7w1HVt8LTLYkz5+5i/E3aeKekR1W0H7Ul2jZ+6nu9l+olAxf/+UCB6eMficVVwduZiic0gIAXfPVczlLnkZWW9jw9I+o+G8+cq5XxaArhxJklg0cRFeLs3HYLk7ufPSxJcc5u9DKDsCQSuxrJzsVSXjVWERnNzHvjiFpMwS9BaBjh3twgKj1cUyK6vOIPP3sfatpizo3mhr9cz7bA8Jx5WKzn1TBvHozHNX0QGYYWdxxM4uphhw1104hYaaxrojRyj5/nvbTzDlafDrbx6XZsD6FwGI8Ivgs4s/a9bC08+7H4N8B9m77Q5DKDsCQSuxrA46sEFwcoS1eLN0drxOPVOHWcXtdIArS9A9qVd0tqYVKObvnTyIx2YOOacVHYD4vr7E9rGtiW1XFFNUubkR/Ogjirn8t/+LvsLGRAQXd2OxQUt2fQintwNGhWfVpav44pIvmBs9l2sir8HNyWwpPlR0iJ05O9v0HtoToewIBK3EMhOrQXDyiLHYg3W9ks5SdsYN8MfdRW0abzySp7AwCc5N6hWdv48pFZ17Jg3kiYuEogNGy+jrs+MU1cgbw0Wt6rJiit6XXqqoY6UvLCR97t1kP7+QvDfepDoxsfmU+AEXwsjblXM/3w+1xhIZkiQRGxTLglELeGHCCzwY/6BCdFniMlFBWSDo7igzsawsO+Ob7x1jicEgKyw7gZ6uhPu7t32DNqBxVnN+RKBpXFxVS2JGcTMrBLYgyzL70ot5Zc3hLmkf0Ba0tXru+XxvA0XnHxcO5N8XDxWKjgWDQ7z47t4JzVp4DLIBZ3XX3GollapBKro2KYmSr7+mcPlyTt1wI6dmz0F37FgTZwBmvgRevczjwjTY/HKjotcNvo5AN/P3yb68fezO2d2m99BeNK+SCgSCRtEb9BwvOW4aR2Yb4GzheJWLhMtA291Yx/MrKNPWmcaj+vl26g1l2rBgRVrs+kN5jOrX/j25zhWO5pbz2LdJDWqwLNt0nNg+Prw+O84hCuzJssz+jBLWHsyltLoWHzdnJg8J4v2NaWw+qlR07j5/AE/OEopOYwwO8eKn+yaSmFHCX2d/l3tOFXE01+guqjPAkt8P8eFto7tkf2ovT3Bygrq6Ro9rU1M5dfMtTde90vjAZW/BqhvMc9v+C8OvgrARSlEnDXdG3clre14zzS1LWsbYXvZZujsCYdkRCFpBZkUmWr3RlOtRLeNfZhGc3NsHSWX7R6sziwk2xpQh1l3QRdxOazmaW851yxKaLDbXme0DmuNobjlXvreNa95PYNmm46Z+Ttd/sKOBonPXxAE8fekwoeg0g3Uxxc/uGoebs9k9/NfBXBKOFzRzho5BlmWynnyqSUWnHkNZGVlPPd205XHILIi+zuLEeqM7S98we3P2kNkEaAJM4z25exzCuiOUHYGgFTTrwho80K5zdVW8Tj3B3hpiepvN8IdzysksrurUPfQE6tsHWFrpGqMz2wc0RksKmSV3TuzPs5cJRcdeQn003DNJ+T2w6NdDnR4Pp01KQpuaaptsSgra5OSmBWa9Au5mJYacFNi2tIGYm5Mbd0bfqZj7v6T/s2kPHYlQdgSCVmCp7AywKoyqibfPZGsZr+OiVhHd27YMj/ZkqlVj0Ie/TuxWcSaOgCO2D7DGVoUMINDThWeFRafV/OPCgYR4u5rGB7PL+H5fZqfuoXy9fU04y9etb/qgRyDMelU5t/lVyD/SQHT24Nn4a8yu8F05u9iTs8euvbQ3QtkRCFqBMhOr9ZWTiyprOFFQaRrH9PHB1UndzIqOITLYUzHefaqYZZuOc837CVz53rYud7t0JG0JJi6urGHj4Tze/OsID67ab9d1O6t9gCX2KGQFFTUk2SgraIi7ixNPXDRUMffan0eo1LWsaLYX+jL7mnC2KB99LQyeZbGgBr65A9Y+byw6uG4hZOzG3cmNO6Mcy7ojApQFglZgadkZlG2+KUpO4BI5tLEljdJV9XUsOZpbzlOrU5o8Xh9n8t29ExwisLY9sSeYuKbOwKHsMhIzStifXkxiRgmnClvv7vtqVzoeLmoujg4lIrjjf68Gg8yXO9LtWvPXwVziwzv/b7KncHV8bz5JOEXKGePfV365jg82H+eRmUM65fpqb/uacLYoL0lw2Zvw3jbQnVWM8g8aX/VsfQvC4plz2Vt8rPGnSGtsMbIzZyf7cvcxMmSkXXtqL4SyIxDYibZOS3q58abhrpUJKTEf0/T2RlLbbpmxjtcZ2ck3FnvjTH68b2KPcWvUx6409d6TM0u58t1tzIoO5VRhJalZZdTUGRqVbQ3FVbW8/tdRXv/rKBHBnsyKDuXi6FCG9/Ju9HfcWPbUjOEhxPdtOnsvvbCKrWkFbEsrYNvxAkqqHLefU09EpZJ49rLhzPlgu2nuw79PcMPYcMJ83ZpZ2T54TZtK4fLltstPn9aykHcYjJ8PmxpPPwcgaz/un13J7ZPn89aRL0zTy5KWsXym7ftpT4SyIxDYyfHS4xhk401vQG7bgpO72rLTmjiTnvCkb6uSV12r54f9Zzp8P2l5FbyzIY13NqTRL8Cdi6OMis+Is4qMrRao4soaEo4XmhSc9KK2BZp3Zj+nnsrYAf7Mig7lj1Rj5VFtrYFX1xxm6Q3xHX5tTVwcmuhom4KUJTc3XAYPbvmksgxH/2xZTlvKDclr+MTbz9RDcEf2DhLzEhkRPKLl9e2MUHYEAjuxbBMxwLpyctwom89TU2cgKbPENO4X4E6Ql2vTCzqAtXbGjfQUt4Y9Sl5TuDqpiOltbAMQH+5HXF8f5q/cR/KZls+rcVahrW3cSnS6sIoPtpzggy0n6OWjYewAf9YdzKWypvEmjsmZpVz2zlb6+buTll9Be8aTd3Y/p57Kv2cNZf2hPGr0xv/zHxOzuGPigA5vISFJEmFLFnPq5lswtBCPI1dXk7vwBXq9vKR5623mHsiyLT7NPWs/tw1/hLePf2eaW5a0jA9mfGDT+vZEKDsCgZ00m3ZuR3DygaxSdBZukVFdoETY66boKW4Ne5U8gAGBHsT39WVEuC/xff0Y2surQWXc1+fENesaA/DWOPHdvRNwUav4IzWHNanZTQYCZ5dq+Skxq8W91dQZOJbXfM8jL1cnzhvoz4HsMrJKtC2esyv6OfVU+gV4cMfE/ny45YRpbtGvB/n2n+M73C3sGhlJ/y9WkvXkUy1aeEp/+glN1HD8b7utaaEjv9l1/RuravjE1YdSnfFvPCErgaT8JOKC4uw6T1sRyo5AYCeWmVgDLJQdSQ2uQ6NtPk+DYoL9O1/ZsddN0VPcGvYqbdeO7M0bc0a0KFffPqAxlxMYFYjXLIKe7508iHsnD+JMSTV/puawJjWH3aeL2sU646yWGBnux/kRgUyMDCS2tw9OalWLsUpgVMi6qp9TT+X+qRF8tzeTosoawBiv91tKNpfFhnX4tV0jI+n/7Tdok5MpX7cefVkZam9vvKZPo+THnyhZtcokm/vKq7gOHoLHeeMaP1l1iV3X9tBVckfUHby9723T3LKkZfzf9M7NzhLKjkBgJ/WWHY1OpleRed41zAvJyfaP1L70rs/EmjE8hGWbjrcseJae4tawV2kL9tbYLNtY+wAfN2dmDg8xxeBY09vXjbvOH8Bd5w8gr1zLXwdy+SM1m4S0QuzRe/w9XLgmvjcTIwPPNnlt+Pdor0ImaB+8Nc48PGMwz/5otq68/P/t3XdYU2f7B/DvISGEEfaMLBUUmQ7ce2utorbu+Tre2mor2lrrqv5866qj2lqtWttqrdVqq7ZunK2jLkQQcSAqoGzZKyE5vz+QDCCbYfD+XJfX1Zycc+eBcpI7z7ifE/fRp4UL+Ka1X26CYRiYh4TAPES5R4Xv7w/Ro0couvmqDo5Egufh4fA+eBA890ZVA5nb6vbC5rYY4zcGP8X+JOvdufz8MqIzohHsFKzh4ppDdXYI0UFOSQ4yijMAAN7pyjcQ38dL6zgsyyr17AjMuPCtg+XHlbXysFW7iaGihjSs0cJVtyW5uiZ5lbcPmD/AD6087bTqKXEW8DG+gxd+mdYBw1tX82GjxoBAVyx+2x89mztXm+hUqEjIDn3QCe/3aIqx7T3xfo+mOPRBJxye2ZkSnVoypq2HUk2r5Oxi/Hj5af01CABjaopGmzaC6+oqOybJyUHyhx9CWlxc9YLmg3R7Ab+3YWlqiYn+ykNjW+9s1ae5eqNkhxAdKBUTTFH+zm0eon39iOTsYqTllcoet/S0Bcek7ocMGIbBuhEhsOar75Ey5TANZljjeU4xVh6/p/nEV+ozydOlRwnQrcfKkISM6IfLMcGiQS2Ujn17Ph4Z+aUqrqgbXAcHuG/eDMZMvkCiNC4OKYuXVC2u6R4KCLVcSSZsDTQqX7Qx1m8srHnyLxmXnl9CTIbq+l41jZIdQnTwMPuh7L+rLDvvoEWNilcqD2GF1uMu4xXDGup6eMQSFiXi6lcDGZOsglJM2HkNqXnafbjU99yVvjr2KDWUYcaGrEdzZ3Rv5iR7XFBahq/OPFRzRd0wDwyA2/L/UzqWd+wYXv7wo/KJDAMM3Vq+G7o6Jhwg7Nvy8wFY8awwwX+C0infRdfdvB1KdgjRgaqVWIwJYObfUus49b3TeWXVDWtU7s344micUe+TVVBahsk/3kBChnx7Dg4DNHa0qPb8EHebeq8a/aYOMzZ0iwe1UOrJ3Xc9EfdTddvaoTbYhIXBftIkpWPp69ej4NJl5ROdWwBTTqnv4ZFKgJcJSofGtRgHAU9+P/2d/DdiM2MNbrc2KNkhRAcVw1hmIhaNsuTHzVwtwfB4WsdRTHZMGCDEo+43/6ys8rDGrintYGchHxa5/vQlTsWmqonw+ioRS/Df3TdlZfuB8i+cG0a1xLmPe7y2c1e0HWas7x4oohtfFwHGtPOQPZaywIpjr8eXCed5n8CiQwf5AakUzz/+GKLESluNOLcApp8Hpp0FuswB2vwHaNxD+ZyIz4EykeyhgCfAhBaVenfqaM8sSnYI0ZKUlcoKCnqlAyYK70t8H0+t4xSUliEuRf4trrmrNQT8129Jt415+eoRRatO3K/RLRPqQplEitn7buPK4yyl48uHBCCsZaPXfu6KpmHG16EHiuhuTp9mECgksf88ysSFBxn12KJyDJeLRl9tgGkj+eR4aW4ukmfOgrSwsNLJTPkcnj7LgMEbgXEHAPum8udfPgZu/qB0yTj/cRCYyv9WLyRfwL0s7efQ6YuSHUK0lFKYgqKy8vL7jSsXEwxuqXWcO0k5kCpcHlrPQ1jqjGnniSZOlrLHz7KKsPvq0/prkI5YlsWiQ3dxKla5iOCcPs0woaN3/TRKD7R6quFxsDLDh718lI59cewexJL6/zLBtbOD+7ebwfDlE+RLHz3CiwUL1fc+cXlA3+XKxy6uBorlPdnWPGuM9x+vdEpdrMyiZIcQLamvnKz95OTXbb6OOqYcEyx6S3n1yNdnHyG7UKTiitfLmpMPsP9mktKxyZ288VFvHxVXvL5e9x4oortJnbzhaS+fM/Y4oxB7r+m2M31t4fv5QbhyhdKx/NOnkbVtu/oL/QYBXp3lj4uzgb/XKZ0yrsU4WJnKl+BfSLqAuKw4Q5usFiU7hGhJMdlRWollApgFhWodx5iSHQDo5eeMzj4Ossd5JWXYdPaRmiteD9suPsZ3F5ULJoa1FOLzt/0pQSCvBTMuBwsG+ikd23jmIXJ13J2+tli/9RYcpk9TOpaxaRPyL1xQfRHDAP2VkyRc3w68fCJ7aGNmg3EtximdUttzdyjZIURLFcmOqZiFh8LQupmzOUzMtNvAUypllZadOwvM4G5nXqPtrGkMw2DRW/5QzA/2/PsMCRnq92KqT/tvJGLViftKx3o2d8K6ESEwqYd6RoSoMiDQFe285aUnsovE+Obc6/Nlwik8HJZdusgPsCxefDIPpQlPVF8kbAUEj5Y/loiAM8uUTpngPwGWpvIh8nNJ5/Dg5YMaanVVlOwQoqWKlVieGQBHcXJyU3ftY6QXIF9hT6I2XsYxDOEvtMaINvKfs0zKVkkmXhcn76ZiwR/KxcpCveywZVybKht3ElLfGIbB4reVh4p3XX2KJ5mFKq6oWwyHg0br18HUU74IQ1pQgORZsyApUPOFp/cSgKtQFPPeYSDxmuyhjZkNxvqNVbqkNnt36M4nRAtiiRhPc58CqGa+TlBLreMY2xCWoo/7NYcFT76HT8S9NFyttMKpvl15nImPfr2tNAHcz1WAnZPbwpxX+/sPEaKPYHdbpa1BxBIWq0/U7hwWXXBsbODx7WYwFvL5RaKEBDyf9ymKbt9G+voNSFm6DOnrN6A4Kqp8ErONO9BxlnKg04uguMvtRP+JsODKY55JPFNrvTuU7BCihYTcBJSx5T0ylZMd8w69tI5TOdlpbUTJjos1HzO6N1U69sWxe5BK6782CABEJ+dg+q6bECmsZvG0t8DuKe0azG7tpOH6tL8f+Kbyj+RTsa/XlwkzX18IV69SOlZ4/jyejRmLrB07kLN/P7J27MDT0WPwdMRIlD56BHQJByyd5Rck3wBi/5A9tOXbVpm7sy16W620n5IdQrSguCeW0rJzBjBr2aGaK5SxbPlcnTP35EX5uBwGAW66bUhZ36Z3bQJXhf2aYl/k4Y/bz+uxReXi0wsw+ccbKBTJt7RwEphhz9T2Ou8vRUh9cLXh471uyl8mFh6KxuoTcVh4KAZrTt5HZGJ2vRYetO7XD44fvK/xvJK7d/F03HiUJqYCPRcqP3lmGSAukT2s3LsT8SxCaVuemkLJDiFaqJiczC1j4ak4OdmJDxO++g/Th2n5CPv2MoZvuYJchfk6ZRIWI7ZdxcO0/Fppc20w53Ewr39zpWNrT91HkahMxRU1ryJxXHPyPhYeisGSw3cxettVvFRYDi/gc7F7Sjt4OlS/FQQhr6P3ujeBi7V8scOTzCJ8dzEBe68lYuuFxxi+5QrCvr1cr+8ZDjNnwkSgua6TNC8PLxYuAttyPOCkMCcpJxG4Lu+9seXbYozfGKVrt92p+d4dSnYI0UJFsuORAXAVan7xmzRScUW5h2n5eHfrFUQn51b7fHRyLt7desWoEp5hrRohsJG8RyotrxTb/05Qc0XNUUwct154jL3XEvHzv8+QqZDo8E1N8MPktmhhZL1mhFjwuJjYwVvtOfX9nlEaEwNpvnavXRITg5LYe0C/L5Sf+HsdUJgpezgpYBLMufJVqaefncbSy0ux/OpybLy1EXcy7hjco0XJDiFaiM8p3yaiSeWdzoOCVV7Dsiw+OXAHeSXqez3ySsow74DhN3NdMTFhsHiQv9KxbRcTkJZXouKKmqEpcayw+C1/tPWuv13kCdEXy7I4qcX+c/X5npF/9pxu5585C/j2AZoqzG0szQMurJY9tOPbYbTfaKXr/oj/AwceHsDOuzsx/vh4jDk2RrZdjz4o2SFEg3xRPlIKUwBUsxKrXQ+V191OytH4wVzhTnIuopJy9G1inevQxAH9/F1kj4vFEqw7VXs1MrRNHAHgwK0ko0kcCVF0OylHabNaderrPUOSp9vu7LLz+30BMAopx80fgAz53Jzu7t3VxonNisXEkxP1Tngo2SFEg4peHaDynlgs+K07V73glYh7aSqfq85pHc+vbwveagGuQoG+g5HJiH2h3Ru1rhpy4khIBWN4z+BY6zY8LDvfJQBopbAnFisp3xUd5V9m1t5YqzFWvigfSy4v0evLDCU7hGhQMV+HI2HhmS4/znPkw8TSUsVVQG6xbiXfdT2/vjV2tMSEjl6yxywLrDgWVyu9KsbwIUCIoYzhPUPQW/tSGwAg6KOwb2DPxYBC1WQ8PAE8+RvRmdGIzYrVKt7drLuIyYzRfGIllOwQokHFMkj3TIAnX9kMfmM3tdfpWtvFGGvBzO7tq9TuK4+zcDYuXc0V+nmp48ajxpY4EgIYx3sGPyQE/MBA7c4N8Ac/WGFeo8ClvPaOolOLcP6ZbvOAziXqdj5AyQ4hGlX07FSZr6Phhu+rMKdFG/10PP91YGvBw0e9fZWOrTweB7FCYT9DxaXk6dyzY4yJIyHG8J7BMAyEq1bCRIvhLBPbaoqmdpwFCITyx6nRyEu9rVMb8kS6zRsCKNkhRC2WZWUFBRtXrpysZnIyALTysEWwu41WrxPiboOWHrb6NLHeTejgBW+FejYJmYXYey3R4LhSKYvtfz9G2ObLOvfsGGPiSIgu7xkcEwamnPrZV8/M1xfev+zR+IWv6PJlvPzhB+WDPIvyfbMUWCfd0un1rXm6l5WgZIcQNdKK0pAvKq8pUblnxyy0m9prGYbB2neDoWmTbWs+F2tHhBjFhqDV4XFN8NlA5Y0MN555iNwi/YeSkrOLMPb7f7Hy+H2l7R+0YcyJI3mzMQyDdSNCYM3najxXImUx7vvruKvl6q2aZubrC+8Dv8F7/z44TJ8O21Gj4DB9OqzDhiidl75+AwqvXlW+OHg04Cof3uqZo9vQdy9P3eYNAZTsEKJWxRCWiZSFl+LkZHtTcLSoIioqY6Fu66gQdxscfL8TmrlojvU66x/ggnaN5bVtsovE2Hz+kZorqseyLA7dTsbAjf/g34SXSs9ZmXFhbqp+M09jTxwJaeYiwMH3O6ns4VH8084tFmPc99cQo+VKxZrGMAzMQ0Lg/PFcuP3fMjh/PBfCFStg2amj/CSpFM/nzIX4ucK2MiYmQP8VsofBpSIEaFmFPdAhEEGOQTq3VXP6SMgbrGIIq1EWYKZwL/K9XbW6/lClfaO6+DjA08ESNuam6OfvgpYetg3ig5lhGCwZ5I/Bmy/Jjv105SnGd/CCl4PqFWuKsgtFWHz4Lo7FpFR5rquvI9a+G4K8EjE+OXCn2mXoIe42WDsixOgTR0KauQhwZGZnRCXl4PS9NOQWi2XvGaViCabsuomiV/vAlSc8/+Lnqe0R8hr0aDJcLoTr1+PpO+9C/OIFAECSk4PkDz+C195f5NvrNO4GNBsIPDwBBsAX6RmY6OGBfFZ10iPgCfC/zv/T6z2TYan6FvLy8mBjY4Pc3FxY61hDgDRsC/5ZgKMJR9EtRopZR+XDKc4TBsBh0Vdqry2TSNFh1TlkFpQCKB9jv7awNxytzNReZ8zm7o9S2hj0rSBXbBnXRuN1Fx9mYN6BO0jPL1U6bsY1wYKBfpjY0Rsmr8YDWZat9kOgoSSOhGhy4+lLTP7hutLGtwI+Fz9Pbf/aDOEWx8bi2dhxYEvl97TNsGFwW7lCfp9mPAS2dCivuQMgnsfD4hadEJtXdfuZQIdA/K/z/+Bj56N0XNvPb+rZIUQN2TYRlVditVU/XwcALj/OkiU6ANDN17FBJzoA8En/5jh+NwUl4vLE8HhMKm48faly+4ZikQSrT8Rh19VnVZ4LbGSNjaNawsdZuaeGYRi08rRDK89qVnoQ8gZo622P3VPbYdIPN1BQWt4Tkl9ShgnfX8Puqe1ei3vDPCAArsuWIWXBAtmx3EOHYB4cBLsxrzb+dGoGtJ0KXN8OAPARifBrPoOYt37BucRzyBPlwZpnjV6evRDkGGTQlxlKdghRoUxahoSc8m8YlVdi8dtrniB3uNIQ1tBW6jcNbQiEtub4b9cm+PqcvOr0gt+j0cffBXklZbAxN0Vffxe08rBFzPNczNkfhccZhUoxTBhgZk8ffNjLFzwuTSskpDptvOyxa0o7TPrhujzhKS3DhJ3XsWtKO7Txqv+Ex3bYUJTERCN776+yY6krV8GsuR8sWrcqP9D9M+DOfqC0fGiaSTiP4Jx0BLcJr9G20DAWaBiLVC8hJwFhR8LASFns2iAB/9XiIlNbLnz+VV/Bs7C0DKFfnEGxuLx71pLHwc3FfWHOUz/BtiEoLC1Dj3UXkFFpSEqRi7UZMvNLIan07uPlYIENI1u+Fm/UhBiDyMRsTNp5Hfml8rkuVmZc7JrSFm286n9DXFYkwrNJk1F8W15Lh+vkBO/fD8LU2bn8wOVNsq0jAAC23oB/WPmGoea2QPNBgHuo8uzsV7T9/KavTYSo8DCnvHKyWzZkiQ4A8L0113CJuJcmS3QAoH+g6xuR6ACApRkX4zt4qj0nLa9qojOmnQeOf9SVEh1CdNDa0w4/T2sPgcJy9YLSMkzceR03n75Uc2XdYHg8NNq4ERwnR9mxsowMPA+fA1b0qn5Wu/cAW4X3jJynwJVNwK0fgUtfATv7ADt6AulxereDkh1CVJBVTk6pNITl36K605VUXoU17A0YwqrAsizO3NO+boa9hSl2TAzFquHBsDSjkXVCdNXSwxZ7pionPIUiCSb+cB3Xn9R/wmPq4gz3TZsArrx9xZGRSPvy1eafpnyg7XT1QV7cBn7or3fCU6/Jzt9//43BgwdDKBSCYRgcPnxY6XmWZbFs2TIIhUKYm5ujR48eiI1V3iystLQUH374IRwdHWFpaYkhQ4YgOTm5Dn8K0tCwLIs7GXdw8slJAEDjtErJTmhXtddn5Jfin0cZssdOAjN0auqo5oqG5XZSDmJ0KHS2YVRLncvkE0KUhXjY4pdp7ZUKEhaJJJj843VcS8iqx5aVs2jdGi4LPlM6lr1nD3KPHCnfRTj2D81BSnKBwx+Un6+jek12CgsLERISgs2bN1f7/JdffokNGzZg8+bNuHHjBlxdXdG3b1/k5+fLzgkPD8ehQ4ewb98+XLp0CQUFBXj77bchkUiqjUmIOvHZ8RhzbAzGHx+PxPzyLQ+qrMTq0EdtjKPRL5QKCYaFCMHRVEa5AdF1H6trr8E3T0IagmB3W+yd3kFpb7jyhOcGrsRnIjIxG2tO3sfCQzFYc/I+IhOzUZfTdu3GjoVNWJjSsZTPl6LkwsHynhttvIgEnuu2vQRQz6uxBg4ciIEDB1b7HMuy2LhxIxYtWoThw4cDAHbt2gUXFxfs3bsX7733HnJzc7Fz5078/PPP6NOn/ANoz5498PDwwJkzZ9C/f/86+1mI8YvPjsfEkxNl20MAAMOyaKzw2Z1pDZgyL+ED1RP/3sRVWIp03XGcdignpOYENrLBL9PaY/zOa8h5tWVLsViCcTuvVekQ2XrhMYLdbbCujopxMgwD1/9bhpJHD1F6r3w4ii0tRfLiNfDuxoBrpmXidf9o+YRlHby2c3aePHmC1NRU9OvXT3bMzMwM3bt3x5UrVwAAt27dglgsVjpHKBQiMDBQdk51SktLkZeXp/SPvNlYlsXiy4uVEh0AcMkGLBQWFT12ZbDk8hKV34YeZxTgjkJ1X19nKwQI36wVfrruOE47lBNSswIb2WDvtA6ws5DfW6o6cKKTc/Hu1it4mJZf/Qk1zITPh/vX34BjI98OQ5xViBdX7MBquw1ecY7ur6vzFXUkNTUVAODiojyW7+LiInsuNTUVPB4PdnZ2Ks+pzqpVq2BjYyP75+HhUcOtJ8YmOjMasVmxVY5Xrq+T4MrgbtZdxGRWv/T8SDW9Om9aVV9d59/QDuWE1Dx/oTV+mdZeqyH0vJIyzDtwp86GtHjujSDcsL58j6xXCtP4yIjRsnfJ3Fbn13xtk50KlT8oWJbV+OGh6ZwFCxYgNzdX9i8pKalG2kqM1/nE89Uerzxf58mrLbHOJZ6rci7LsjgUpZzshLUU1kwDjUgrD1uVmxhWRjuUE1J7SsqkkKjbiVjBneRcRCXl1G6DFFh17gynOeFKx7LiBMhN5KM40xTpdwRIuWGD9DsCFGeaKvdM+b2t8+u9tsmOq2v5p0rlHpr09HRZb4+rqytEIhGys7NVnlMdMzMzWFtbK/0jb7Y8UfVDmU0qdRAmuDIqz49MzEbSy2LZ43be9nC3s6i5RhoJhmGwbkSI0qqQ6tAO5YTULl0XC5zW8XxDOUybBoHCNBQAeHHFDk/POCErToCcx5bIihPg6RknPI1wRGkuFxC2Bhpp3m+vstc22WncuDFcXV0REREhOyYSiXDx4kV06tQJANCmTRuYmpoqnZOSkoK7d+/KziFEG9a8ahJellVadp4pAPIsGZXnV66t86ZNTFbUzEWAg+93UtnDE+Jug4Pvd6IdygmpRa/7YgGGYeC2ciV4TZsqHq323JKXPDw964TSkM+qraSsSb2uxiooKEB8vHwPnSdPniAqKgr29vbw9PREeHg4Vq5cCV9fX/j6+mLlypWwsLDA2LFjAQA2NjaYOnUqPv74Yzg4OMDe3h6ffPIJgoKCZKuzCNFGT8+e2Hl3p9Ix5xzAqkT++Imr/Abr5am8N5aoTIqj0SmyxzyOCQYFudVKW41FMxcBjszsTDuUE1JPjGGxAMfKEo2+/hpPBg8GpOpnKEtFDF6s2wnv3/rp/P5Rr8nOzZs30bNnT9njuXPnAgAmTZqEn376CZ9++imKi4vxwQcfIDs7G+3bt8fp06chEMi/DX711VfgcrkYOXIkiouL0bt3b/z000/gcN6M0vykZgQ7BiPAIUBpknLlYoIVQ1iBDoEIcgxSeu7iwwzZMk8A6OnnBBsLWmVEO5QTUn/6+rtg64XHWp9fKpZoNS+2prH5eRoTnQolMTEoiY6GeUiITq9BG4GCNgIl5eKz4zHy6EiIJSL4vgAmnpGg+Qv586vfYfAo0Ba7B+yGj52P0rUzf4nEsRh5z85341tjQOCb3bNDCKlfLMsi7NvLiE7WvqL5W0GuWPNOMAT8uvuylr5+A7J27ND6fIfp0+H8cXnnCG0ESoiOnCyc4J4uwcqfJFixWznRAYDJ/wC7XN6rkujklYgRESef2GfN56JHc+e6aDIhhKik7WIBRcdjUjFk82XEpdRd/TmJjrXudD0foGSHEJmIY2ux5GcRfFSUaHJNZyENX4XSa6eUjp+MSYWoTN4FOyjYDXxTGkYlhNQ/TYsF/N2sEdRIuUfkSWYhhn57Gb/dqJuyLBwdR1R0PR+o5zk7hLwupBIJrL/+Q2lCcrXniRi8+HQevM/3BfOqIFaVVVgt39xVWISQ14+mxQISKYsNEQ+xRWF+T2mZFJ/+Ho0bT19ieVggzHm19wVO0LuXTsNYgj69dX4NSnYIAXDrjzXwSNVu+lpJmhglZ3+Ded/RSMktxr9P5DsKN7I1R1tv1ftmEUJIfVC3WIDLYfDpAD+Eetthzv47SkvQD9xKRszzXGwZ1xpNnKxqpW38kBDwAwNRcveu5nODgsAPDtb5NWgYSwssy+JOxh1svLURy68ux8ZbG3Eno+5Ka5PaF3/qiE7n5//5GwDgz6gXSpU9w1oKYfIG7XBOCGk4evm54OiHXRBSacjrfmo+hmy+jGMK5TVqEsMwEK5aCRMNw1Mm1tYQrlyh12oxSnY0iM+Ox5hjYzD++HjsvLsTBx4ewM67OzH++HiMOTYG8dnxmoOQ11paYRoyCwt0ukaSX75pXuUhrGFvcCFBQojx87C3wG8zOmJSRy+l4wWlZZi5NxLL/oxVmqNYU8x8feH9yx7wAwOrfZ4fFATvX/bAzNdXr/g0jKVGfHY8Jp6cWGUn7AqxWbGYeHJitUuRifH4/dHvyOfr1kvHEQgQl5KH+6nyv40AoTV8qSIwIcTImXE5+L+wQIR62+Oz36NRKJLInvvpylNEJeXg23GtIbTh43ZSDiIU5gH19XdBKz2Lhpr5+sL7wG8oiY5G/pmzkOTlgWNtDUGf3uAHBxtU/4fq7KD6dfosy2LMsTHV7oRdWaBDIPYO2ksVYY2QWCpG/4P9YRufjhW7JZoveMX7m6XYWBaCbRcTZMcWD2qBaV2b1EYzCSGkXjzOKMD7e27hYZpy77eAz4WDFQ9PM4uqXBPsboN1I0LqZDsYqrNjoOjMaK0SHQC4m3UXMZkxtdwiUhsuJF1ARnEGHgmBeFftruG7mILXcwSO3JYX4mEYYHDIm7fDOSGkYWvqZIXDMztjeGvlIfr8krJqEx0AiE7Oxbtbr+BhWvWjIvWBkh0Vziee1+n8c4nnaqklpDbtv7+//D8YBtveMgEL9R2dJjwWwi/X4tqzbKTmydepd27qCBdrfm02lRBC6oUFj4v1I0KwengQTDnajWDklZRh3oHXZyEPJTsq5Il0q9Co6/mk/iXkJuBa6jXZ45B0HhgVO+4C5T063js2wax9fxymHc4JIW8QhmEwup0nVg0P0nzyK3eScxGVlFN7jdIBTVBWwZqnW4VGXc8n9e/AgwOy/zaRsnjnn2JAIdkRBDqBY2UGjkAAwZCR4PceCcbEBCViCU7EyMss801N0D/ApS6bTggh9eJxRqFO55++l/ZabARMyY4KPT17YufdnVqf38uzVy22htS0InERjsTLa+t0iwXMc+WJjpkLH40OXKx20vnZuHTkl5bJHvf1d63TTfMIIaS+KBYcrI3zawsNY6kQ7BiMAIcArc5lwKBMWqb5RPLaOPHkBPLF5ZPnGCmLsZeVV2I5Tv+PytV1VWvr0MRkQsibwcZcty92up5fWyjZUYFhGHzR+QsIeJqXzrFgMfPsTNzLulcHLSOGYlkW+x/slz3ucJ+Fbbb8eZ4DF4IxM6u99mWhCBcepMse21vy0NXXqdbaSgghr5O+/roN2ffT8fzaQsmOGj52Ptg9YLfKHh4OI98YrUBcgPci3qOKykYgJjMGcS/jAAAMy2LMZeVqoI6TR4PhVL/p3bGYFJRJ5asLBge7wZRDtxEh5M3QysNW5Q7q1fk3IUvzSXWA3qU18LHzwa9v7cUvbRZhqlVzjDBrhKlWzfFLm0XYM3APrEzlG6PllObgvxH/RWJeYj22mGii2KvT9iEL10z5czxbE1hP/lTltbQKixDyJmMYButGhMCar92U3zUnH2Dtqfv1vgSdJihrkh4HHJoB35hYuD7nQyIyAYcnhaDRBfCDAvBt90/x3s2VKJGU11zJKM7AtNPTsGvALrhZudVz40llOSU5OPnkZPkDlsWIS8q9Og5jh4AxrX6MOTGrCLeeyce7vB0s0NLDtraaSgghr6VmLgIcfL8TPjlwB9HJuVWe55uaoEQsf2/99vxj5JeUYdnggHrbKJmSHXXS41C6fgBe/MNFyUvleRlZcQLwI5MRkBCOTZO+xKxbayCWls86TylMwfSI6fhpwE9wNHesj5YTFQ7HH4ZIKgIAtIln4SWffgNTa8Dmvc9VXxtVtVeHtgghhLyJmrkIcGRmZ0Ql5eC0wt5Y/fxd0NjRElN33VT6crj76jMUlJThy3eDwa2HoX8axlKFZVH6/XQ8Pc5HyUtetaeUvOTh6XE+2hz+Buu7r1Oaw/Ms7xmmn56OnJKcOmow0UTKSuVDWCyLdyrN1XF4pw8YM/Nqr2VZtmqy05KGsAghby6GYdDK0w7zB/hh5bAgzB/gh1aedrC14OHnqe3Q1Vf5y/4ft5/jg18iUSLWfh/CmkLJjgps0g28OJoCqVj9r0gqNsGLoynowQiwsstKpQq88TnxmHFmBgpEBWoikLpy5cUVJBckAwBCnrDwSZE/x7VkYTvrC5XXxjzPRYJCMa1WnrbwdrSstbYSQogxs+Bx8f2k0CoFV0/fS8PUXTdQWFq35Voo2VGh5NRulT06Vc59yUPJqd14q8lbWNZpmdJzsVmxmHl2JorLimuhlUQXsn2wWBbvVJqr4xjWCYyl6hUGVWvrUK8OIYSoY8bl4NuxratsIno5PgsTdl5DblHdFRykZEeF/MgE3c6/9RgAMNx3OOa3na/0XGR6JMLPh0MkEdVY+4huXhS8wN/P/wYABDxj4aeQu3AtpLD5aKXKa8skUvx1R77DOdeEwaAgmnxOCCGacDkmWPduCCZ38lY6HpmYg1HbryIjv7RO2kHJjgoSkW6/GsXzx/uPx4etPlR6/sqLK5h3cZ5sEjOpWwcfHoSULe/Neeey8hJIhwEtYWLrqvLaS/GZyCyQJ6rdmjnBwcqsdhpKCCENjIkJg6WD/fFhLx+l4/dT8zFy21U8z6n9kQ9KdlTgeLTQ6XxWJAYU6ghMD5qOKYFTlM45l3QOSy4vkX3okrohlojx+6PfAQB+SSwCE+X/nzh8KWxnr1J7PdXWIYQQwzAMg4/7NcfCt/yUjj/JLMSIrVeQkFG7c1sp2VFBEDZOp/NzbzxD1py3wJa82m+JYRDeOhyjm49WOu9YwjH879//1XuBpTfJmcQzeFnyEgCqzNVx6N0MJi5NVF5bWFqGU7FpsseWPA76tng9yp8TQoix+W+3plg1PAiKVTte5JZg5LariH2RC5ZlEZmYjTUn72PhoRisOXkfkYnZBn9mUp0dFfgtQ8D3a4qS+4+1vIJB+smnKIzrBOG3u8D1aQ2GYbCg/QIUlxXjyGP5DtsHHx6EOccc/bz74ULSBeSJ8mDNs0ZPz54Idgym2i01bN/9fQAA3+csQp4q9OqYSWA3e4Xaa0/fS0WxwjLJAYFuMOdVv5UEIYQQzca084SlGRdz90fJtt/JLBBhxHdX4WbDx2OFla8AsPXCYwS722DdiBA0c9G8X2V1GJa6GJCXlwcbGxvk5ubC2tpadrz00SM8HTMG0oJCNVdXxTWXQrjwA1iOmA0AKJOWYf7f83H62WmN1wY4BOCLzl/Ax85H47lEs4fZD/HOn+8AAD77TYLWj+V/7k69hXD89myVa1iWxe2kHETcS8Ph28+Rklsie27P1Pbo4kuFIgkhxFDn7qfh/T2RKC3TbmqHNZ+Lg+93Ukp4VH1+V0bDWGqY+frC+9dfwQ8MrPZ5flAQPH/6AZbNlasrlxWbIPHzrciY8y5YUSm4Jlys7roa3dy7aXzN2KxYTDw5kTYUrSG/PfgNANA4hVVKdEx4Uth9tKzK+Q/T8hH27WUM33IFWy88Vkp0uCYMHKy0K0dACCFEvV5+LvjpP+1gYapdKpJXUoZ5B+7oNaRFPTvQnBmyLIuS6GjknzkLSV4eONbWEPTpDX5w+ZATK5Ui64twZPx6GmCVh6AsvMwh/O4XmDZugWJxMXr+1hOFZZp7igIdArF30F4a0jJAobgQvX7rhaKyIsw7KEHbR/I/dceu9nDacVnp/Idp+Xh36xXklagudlXdNwtCCCH6++1GEj79PVrr8w990AmtPO0AaN+zQ3N2tMAwDMxDQmAeElL98yYmcPz8a1h0PoTn8xehrED+oVr0rBhPhg2H8PNP8LhrqFaJDgDczbqLmMwYBDsF18jP8CY6+vgoisqK4JnOKiU6JqZS2M9aoHQuy7L45MAdtYkOIP9mcXhmZ0pECSGkBjzJ0m2qyOl7abJkR1s0jFWDLHoPQ5Njp2DV3FbpuKQESFq4Ds+XzAFHon1H2rnEczXcwjcHy7LY96B8YnLlPbDsWluBEzxI6djtpJxqd++tzp3kXEQl5dRIOwkh5E2XW6xb/Tldzwco2alxHBcPuB+6DJcxnQET5cSm8YUU/N8eCZxyWIBl4fucxZgLEkw/KcGYCxL4PmeVavVEpkfSNhN6up1+G/E58WiUyaL9fYVeHa4U9u/PBSr1ykTcS6scQq3TOp5PCCGkejbmprV6PkDDWLWCMTGB/dLvYd5xD54v+gLifPkHa7MXwNqdEuRaAG45ytcNuypBvCuw5W0Okp0Y3E6/jb4H+2Jks5EY4zcGThbKE6GJahW9OsMvS5UyersgU3Dbja5yfl18syCEEFJVX38XbL2gbZkXoJ+/7rXOqGenFpn3G4/GR47C2s9C6biFqGqiU8EnFVi+RwL3jPLeiNzSXOyI2YH+v/fH4kuL8eDlg1putfHLLM5ExLMIuGWx6Bwn79VhOFLYT38fMKlaJ6cuvlkQQgipqpWHLYLdVW/ErCjE3QYtPWx1fg1KdmoZR+gD4YErcB0VUmVYSxWrEuCDYxKlIS2xVIwjj4/g3b/exX9P/xeXn1+usvyOZVncybiDjbc2YvnV5dh4ayPuZOi3TM+YHXp0CGXSMgy7KlX6ldu1YMDtNq3aa/rq+E1Bn28WhBBCqmIYButGhMCar36wyZrPxdoRIXotDqFhrDrAmJrB7v/2gbH4L1J+/Eera3xSgJFFvvjNsmq9naspV3E15Sp8bH0wwX8CBjUZhKS8JCy+vBixWbFK5+68u/ONKlQokUpw4OEBOGez6HpXoVfHhIX9fyYC3Oo38EzPK6n2eHX0/WZBCCGkes1cBDj4fid8cuBOtYtFQtxtsJYqKBtGmzo7FRV1c4vFsDE3RV9/F7TysNUpw0z/cDiyIuK0Pt+hbwsUfLEOe+7twZ+P/0SppLTa86x51iiVlKp8HgAEPAF2D9jd4BOeC0kX8OG5D/HfExL0iZL/adv5ieG6LxLgV/3/ey0hCxN+uA6RFlU8qc4OIYTUHpZlEZWUg9MKn7f9/F3QUsXnLdXZqSEP0/KrzTT12atDkp+v02uXJqWisXVjfN7xc8xqNQu/PfgNv97/VbapZYU8UZ7GWPmifCy5vKTBFyrc92AfHHJZ9IhWyOFNWDiMf6faROd+ah6m7b6plOjwOCYQSaomPoZ+syCEEKIewzBo5Wmncx0djXGpZ0d1ZljTFXV17dkBAPNG5rCfMAqCcXPAmPJQKinF8YTj2H1vN+JzdN9S4pe3fmlwhQpZlkV0ZjSOxB/BgYcHMPWUBP0j5X/Wtr4lcPv1GmDlrHRdcnYR3tl6BWl58h4xRyseDs7oiOwisdbfLAghhNQPbXt2KNlB9b8slmUR9u1lrQrNhbjbaFVRt/jUr3g6e7lebeTZAPZDusBmxlKYOLiDZVlceXEFy68ux4vCF1rHmRo4FeFtwvVqw+soPjteaa6SXT6LzVslMK3YqJxh0XRhT/AmbFW67mWhCO9+dwUJCrvrWvI42P9eRwQ20m5VACGEkPpFG4EaqDYq6vL7jgLfRb+RQ1EukPrzJcT37o3MWf0hjT2LzsJO6Nyos/wkLQoVajPkZSzis+Mx8eREpUnZQ/6VyhMdAJcCGSR2U66rUyQqw39+uqGU6JhyGGyfGEqJDiGENEA0Z0cFfSrqahpjZExMIPxyHZ5Onw2pSHUvkIkpC7vOjZF74wnKCpXPk5SYIONMIjIvzIRtoAWcB/kDANwzWHxwVAKfVOVYlQsVJuUlQSQRgccx7t27WZbF4suLkS/KL0/yXgCdY6VKw1dSAPs7cXAu5jvs9ewGhmEglkjxwS+RuKOQnDIM8NWolujs41j3PwghhJBaRz07KuhaITfiXhr+eZQBcTUTWxWZte8P7x2bVPbw8F1M4f39Jjh/dwI+l25C+NG7MHOuWsCOLTNBdlQJuq28hfn7y/DF7qqJTgXFQoX/pv6LIYeH4HjCcUhZzauPXlfRmdGIzYqFewaLlT9JsGK3BG/dYsFRGJQt5AOmEvmmqlIpi/kHo3HhQYZSrKVv++PtYGEd/wSEEELqCs3ZQfVjfmtO3tepfHUFWwtT9GnhgoGBruji6wgzbtVqvQAglUjw4MBPKDl2ECZFhZBaWII/6F00HzEZJhzla1iWReGxvXi5bSsKH2Xp/gO+Eu8GLJzEke0L1cK+BeaGzkUHtw56x6wvG29txKnz32P5Hgms1JTIKeADn4/noH/PaShO649tfycoPT+zZ1PM6+9Xy60lhBBSG2iCsg6q+2VFJmZj+JYrBsUVmHHRq4UzBga6onszZ5jzypMYVcvZAWhczl4SdQNZG1cg79oDQI//cwsnchDfSHlorLOwM+a0mYPm9s11D1hPFv+zCO2X/KGyN0tRvBuwd9ZbuHqjl9LxkaHuWPNOMK2wIoQQI0XJjg4MXY3FQHPeYW7KQY/mTgjxsMG35x8j38Dl7KLkZLz8ZjWy/zyrU9JzoRMfW7pXfW0GDN5u8jZmtZoFoVX5kE7Fku7zieeRJ8qDNc8aPT17Itix/hIEKSvFoUeH8NsfX2DZT9pXPZ4XFoq7jHyicp8WzvhufBtwOTSSSwghxoqSHR0YWmdn15R2SHxZhJN3U3H+QTpKxIbPhdF2Ofvzcb2Rd0v7pefWAWa4/vmH+Pbhr0grqjoJ29TEFGP9xqK3V2+sub6myvYTAOpt+4nYrFis/HclojOjMeGsBIOva/+ne6ZFM6xv/l8AQBsvO+yZ2l7W00YIIcQ4UbKjA3W/LHVDTtVV1C0SleHigwycuJuKc/fTUVCqOlHS5NAHnTSu8NK5UCHDwqZxMSy6+eP3tgHYmXYZ+eICndtWl9tP5Jbm4pvb3+C3B7+BK5ai320Woy9KYabDr/Z5E0dMC/4Mvs5WODCjI2wtjHs1GiGEEEp2dKLN3li67NVRoUQsweX4TJy4myrbV0sXLdwEGNvOE/5CG/i5CmBpVnUFlyGFCs1sxDDzN8WB3s2xm30GsbRS9vBqSXfoIymsSson+970NcEjIQCGQaBDYK1uPyFlpTgcfxgbb21EfuFL9LrDYvgVKex1z82Q2twBCzv8H35/vxOEtuY131hCCCF1jpIdHWj7yzKEWCLFf3ffxPlKy561xTBAY0dL+LtZI0BoA3+hNfzdrOFoaYqEHiEQpevfg8SYsOB4luJYJyf84FkElmFU1u0BoFS3p7a2n7iXdQ8r/l2Bu+l30D2GxTuXpXDWrsZjtaL7+qP14p/gS/taEUJIg0HJjg7qItkB9F/Oro6TFQ+tsm5h7pkfwYpVT7ZluCyShV5wT3kOVixReV6+jRQXm3HQM5qFpepN1GVLujt2HoklHZbUWO9OxZDVgbj96HRPihGXpHDLrv5cE64U0jLNE4z59iKUbNqGFm371EgbCSGEvB4o2dFBXSU7NbGcXZWu+XfwWeQemGRX/d8ptWewutV4/CMIwaEJgfCKvIicX35E6VMt1m2rUVG3x81KiPZu7dHerT06uHWAo7nqSsRSqRR3//4DScd+B5tfAEZgBY9B78C/61D8lfAXNt38Cj7RWRj5jxQemdXHMHcQwSk4DybmJkiMsIVUTZJnYiqFTZgtXP/3r6y+ECGEkIaBkh0d1FWyo8ty9qBGNvi/If6IS81H7Is83HuRh/upeepXerFSDM65ikGpV2EhLkGRKR/HXDviL9uOAFOeEPg4W+Gd1u5o4WqFZrnJYP7Yg7yTZyDVcyJ1dXV7fGx9ypMf1/YIdQ2FgFc+dPTo1lk8mz8PjZKLq8RJcDPBuUAWvaJZNFGxUwffTgSnoHxYBjcGuoRj9nkxPn+2APnX+Sh5WXXCMd9eBEG7Eizz/hLfzB5L9XQIIaSBoWRHB3WV7ADaL2evrs6ORMriSWaBLPm5l5KH609eorRM/6XujlY8BDvw0DflNoKu/QnOsxSdrk9yAK43Z5BmxyDNlkGaLZAtANhXiYUJY4JAh0C0KXJGtxUnYal9aRwZMxsxHIPyIegQDKbrXMC3PyKTczF8yxX4MslYx92KZjnJyE/mQyIyAYcnhcC9BA9s3TGv7H08Yt21WtlGCCHEuFCyo4O6THYA3Zezq7PmRBy2XkzQfKKWfrnxf7B/nm9QDBEHSLcF0l8lP2k2QP/brMq5N6rwBGVwDMyHda+uYLrNRWmjdohOzsX1Jy+x/0YiEl9W9BCxaMk8Rj/OTdigELmwxGlJKKLYpigv+Qi836Mp5g+gbSEIIaQh0fbzm3Y9rwfNXAQ4MrOzXsvZK+sb4FqjyU6+lSXsYViyw5MA7lmAe5Z+eTRrLoEwuAAWA/ojqvF/cCHHGTdPZCMq+TRE1fZiMYhifRBVprrmj67L/gkhhDQclOzUE4Zh0MrTzuChlVYetgh2t9FqHlBwIxusGxGMuNR83EvJQ1xKPu69yENmgXzZ1XHXDnj/wWGD2mSoe/6W+DJkBS5GW4C9kw8YmHwBgI151Z3jCSGEvBko2TFyDMNg3YgQreYBrRtZPjzWzNUaYS0byZ5Lzy9BXEo+4lLy8Ou/fEijj1S7qqsyqR2D99vMQcviBAQUPoF3USqcCnNgWVgMFECrZeHVKS5mcCHdQq9rVenn71Kj8QghhBiPBpPsbNmyBWvXrkVKSgoCAgKwceNGdO3atb6bVSeauQhw8P1Oes8Dchbw4Szgo3szJ+QWi7E6cTwW/b1Lfd0eUylWt56ERCshEq2E+NOpi9LzVmwhWoofo2VxPEISrsD9mfY/j1TFTg5eDhZo622Pdt72CPW2Q/i+KEQ/19yjFeJug5Yetto3gBBCSIPSIJKd/fv3Izw8HFu2bEHnzp2xbds2DBw4EPfu3YOnp2d9N69O1NQ8oL7+Lth6IQQruk3SULdnEv4RhKCrjyNyisVIyChAoUherLCAscQlXjAu8YIxx+8F3J891fpnyXFxAcMALVyt0a6xPdp626Ottx2crflK560bqV2P1toRIbTsnBBC3mANYjVW+/bt0bp1a2zdulV2rEWLFhg6dChWrVql8fq6Xo31OlOqBaShbo/izuwsyyIjvxSPMwqRkFmAJxmFSMgsxJPMQthkRmLW1e1olKY54XjuwuLy6PmY85/xsOZrnmdTkyvbCCGEGJc3ZjWWSCTCrVu38Nlnnykd79evH65cqb5acWlpKUpL5ZNy8/LyarWNxqTyHKC/7DrjL7vOVc6r3GPCMAycrflwtuajY1MHpXNXHnOGVPwTCs+I1W5BUWgGSEJ54Ht30CrRAWp2ZRshhJCGSb8ZpK+RzMxMSCQSuLgoT0B1cXFBamr12yGsWrUKNjY2sn8eHh510VSjUTEHKNjdptrnQ9xtqi16qMqAIDes5c0Et28hnrtU35H43IUFt28h1vFmol+Aq07trVjZNn+AH1YOC8L8AX5o5WlHiQ4hhBAADaBnp0LlDzaWZVV+2C1YsABz586VPc7Ly6OEp5Ka7DFp5WEL80YBmPd8EdZ224KywnQkpliAFZmA4Unh6VYEB0tnzCubA4tGATSZmBBCSI0y+mTH0dERHA6nSi9Oenp6ld6eCmZmZjAzM6uL5hm1mqoFJB8aK8TQkhVoafYY/ZrKqx3vkIQiStwU1nxTHKTJxIQQQmqY0Sc7PB4Pbdq0QUREBIYNGyY7HhERgbCwsHpsGVGkuDw+KrlqtWOaTEwIIaS2GH2yAwBz587FhAkTEBoaio4dO2L79u1ITEzEjBkz6rtpRAFNJiaEEFIfGkSyM2rUKGRlZWH58uVISUlBYGAgjh8/Di8vr/puGqmkpobGCCGEEG01iDo7hqI6O4QQQojx0fbz2+iXnhNCCCGEqEPJDiGEEEIaNEp2CCGEENKgUbJDCCGEkAaNkh1CCCGENGiU7BBCCCGkQaNkhxBCCCENWoMoKmioilJDeXl59dwSQgghhGir4nNbU8lASnYA5OfnAwDtfE4IIYQYofz8fNjY2Kh8niooA5BKpXjx4gUEAoHa/Zny8vLg4eGBpKSkGqu0bAwxjaGNtRHTGNpYGzGNoY21EdMY2lgbMY2hjbUR0xjaWBsxjaGNusRkWRb5+fkQCoUwMVE9M4d6dgCYmJjA3d1d6/Otra1rfFsJY4hpDG2sjZjG0MbaiGkMbayNmMbQxtqIaQxtrI2YxtDG2ohpDG3UNqa6Hp0KNEGZEEIIIQ0aJTuEEEIIadAo2dGBmZkZli5dCjMzszcqpjG0sTZiGkMbayOmMbSxNmIaQxtrI6YxtLE2YhpDG2sjpjG0sTZi0gRlQgghhDRo1LNDCCGEkAaNkh1CCCGENGiU7BBCCCGkQaNkhxBCCCENGiU7OtiyZQsaN24MPp+PNm3a4J9//tE71t9//43BgwdDKBSCYRgcPnzYoLatWrUKbdu2hUAggLOzM4YOHYoHDx4YFHPr1q0IDg6WFXXq2LEjTpw4YVBMRatWrQLDMAgPD9c7xrJly8AwjNI/V1dXg9v2/PlzjB8/Hg4ODrCwsEDLli1x69YtveN5e3tXaSfDMJg5c6Ze8crKyrB48WI0btwY5ubmaNKkCZYvXw6pVKp3G4Hykuvh4eHw8vKCubk5OnXqhBs3bmh9vaa/a5ZlsWzZMgiFQpibm6NHjx6IjY3VO94ff/yB/v37w9HREQzDICoqyqA2isVizJ8/H0FBQbC0tIRQKMTEiRPx4sULg37uZcuWwc/PD5aWlrCzs0OfPn1w7do1veMpeu+998AwDDZu3GhQGydPnlzl77NDhw4GxQSAuLg4DBkyBDY2NhAIBOjQoQMSExP1ilfdPcQwDNauXat3GwsKCjBr1iy4u7vD3NwcLVq0wNatWw36udPS0jB58mQIhUJYWFhgwIABePTokcp42rx/63LvaBNP13tHU0x97h1t2qnrvaMKJTta2r9/P8LDw7Fo0SLcvn0bXbt2xcCBA1XetJoUFhYiJCQEmzdvrpH2Xbx4ETNnzsS///6LiIgIlJWVoV+/figsLNQ7pru7O1avXo2bN2/i5s2b6NWrF8LCwtR+OGnrxo0b2L59O4KDgw2OFRAQgJSUFNm/mJgYg+JlZ2ejc+fOMDU1xYkTJ3Dv3j2sX78etra2ese8ceOGUhsjIiIAACNGjNAr3po1a/Ddd99h8+bNiIuLw5dffom1a9fim2++0buNADBt2jRERETg559/RkxMDPr164c+ffrg+fPnWl2v6e/6yy+/xIYNG7B582bcuHEDrq6u6Nu3r2x/Ol3jFRYWonPnzli9erV2P6CGmEVFRYiMjMSSJUsQGRmJP/74Aw8fPsSQIUP0jgkAzZo1w+bNmxETE4NLly7B29sb/fr1Q0ZGhl7xKhw+fBjXrl2DUChUe562MQcMGKD0d3r8+HGDYj5+/BhdunSBn58fLly4gDt37mDJkiXg8/l6xVNsW0pKCn744QcwDIN33nlH7zbOmTMHJ0+exJ49exAXF4c5c+bgww8/xJEjR/SKybIshg4dioSEBBw5cgS3b9+Gl5cX+vTpo/L9WJv3b13uHW3i6XrvaIqpz72jTTt1vXdUYolW2rVrx86YMUPpmJ+fH/vZZ58ZHBsAe+jQIYPjKEpPT2cBsBcvXqzRuHZ2duz3339vUIz8/HzW19eXjYiIYLt3787Onj1b71hLly5lQ0JCDGpPZfPnz2e7dOlSozErmz17Ntu0aVNWKpXqdf2gQYPYKVOmKB0bPnw4O378eL3bVFRUxHI4HPbo0aNKx0NCQthFixbpHK/y37VUKmVdXV3Z1atXy46VlJSwNjY27HfffadzPEVPnjxhAbC3b982qI3VuX79OguAffbsWY3FzM3NZQGwZ86c0TtecnIy26hRI/bu3busl5cX+9VXX2nVPlUxJ02axIaFhWkdQ5uYo0aN0vtvUpvfY1hYGNurVy+DYgYEBLDLly9XOta6dWt28eLFesV88OABC4C9e/eu7FhZWRlrb2/P7tixQ6uYld+/Db131H0e6HvvaPMZo+u9o01MXe4dRdSzowWRSIRbt26hX79+Ssf79euHK1eu1FOr1MvNzQUA2Nvb10g8iUSCffv2obCwEB07djQo1syZMzFo0CD06dOnRtr26NEjCIVCNG7cGKNHj0ZCQoJB8f7880+EhoZixIgRcHZ2RqtWrbBjx44aaStQ/ve0Z88eTJkyRe3Gs+p06dIFZ8+excOHDwEAd+7cwaVLl/DWW2/p3a6ysjJIJJIq37rNzc1x6dIlveNWePLkCVJTU5XuIzMzM3Tv3v21vY+A8nuJYRiDevYUiUQibN++HTY2NggJCdErhlQqxYQJEzBv3jwEBATUSLsA4MKFC3B2dkazZs0wffp0pKen6x1LKpXi2LFjaNasGfr37w9nZ2e0b9/e4CH7CmlpaTh27BimTp1qUJwuXbrgzz//xPPnz8GyLM6fP4+HDx+if//+esUrLS0FAKX7iMPhgMfjaX0fVX7/NvTeqenPA21j6nrvaIppyL1DyY4WMjMzIZFI4OLionTcxcUFqamp9dQq1ViWxdy5c9GlSxcEBgYaFCsmJgZWVlYwMzPDjBkzcOjQIfj7++sdb9++fYiMjMSqVasMaleF9u3bY/fu3Th16hR27NiB1NRUdOrUCVlZWXrHTEhIwNatW+Hr64tTp05hxowZ+Oijj7B79+4aafPhw4eRk5ODyZMn6x1j/vz5GDNmDPz8/GBqaopWrVohPDwcY8aM0TumQCBAx44d8b///Q8vXryARCLBnj17cO3aNaSkpOgdt0LFvWIs9xEAlJSU4LPPPsPYsWMN3uDw6NGjsLKyAp/Px1dffYWIiAg4OjrqFWvNmjXgcrn46KOPDGqTooEDB+KXX37BuXPnsH79ety4cQO9evWSfXjrKj09HQUFBVi9ejUGDBiA06dPY9iwYRg+fDguXrxocHt37doFgUCA4cOHGxTn66+/hr+/P9zd3cHj8TBgwABs2bIFXbp00Suen58fvLy8sGDBAmRnZ0MkEmH16tVITU3V6j6q7v3bkHunJj8PdImp672jLmZN3Du067kOKn8LZ1lW72/mtWnWrFmIjo6ukW/jzZs3R1RUFHJycvD7779j0qRJuHjxol4JT1JSEmbPno3Tp0+rHLPX1cCBA2X/HRQUhI4dO6Jp06bYtWsX5s6dq1dMqVSK0NBQrFy5EgDQqlUrxMbGYuvWrZg4caLBbd65cycGDhyo1TwLVfbv3489e/Zg7969CAgIQFRUFMLDwyEUCjFp0iS94/7888+YMmUKGjVqBA6Hg9atW2Ps2LGIjIzUO2ZlxnIficVijB49GlKpFFu2bDE4Xs+ePREVFYXMzEzs2LEDI0eOxLVr1+Ds7KxTnFu3bmHTpk2IjIys0d/bqFGjZP8dGBiI0NBQeHl54dixY3olFBWT5cPCwjBnzhwAQMuWLXHlyhV899136N69u0Ht/eGHHzBu3DiD30u+/vpr/Pvvv/jzzz/h5eWFv//+Gx988AHc3Nz06n02NTXF77//jqlTp8Le3h4cDgd9+vRReq9SR937tz73Tk1+HmgbU597R13Mmrh3qGdHC46OjuBwOFUy6PT09CqZdn378MMP8eeff+L8+fNwd3c3OB6Px4OPjw9CQ0OxatUqhISEYNOmTXrFunXrFtLT09GmTRtwuVxwuVxcvHgRX3/9NbhcLiQSicHttbS0RFBQkNqVD5q4ublVSeZatGih92R0Rc+ePcOZM2cwbdo0g+LMmzcPn332GUaPHo2goCBMmDABc+bMMbjHrGnTprh48SIKCgqQlJSE69evQywWo3HjxgbFBSBbJWcM95FYLMbIkSPx5MkTREREGNyrA5T/bfr4+KBDhw7YuXMnuFwudu7cqXOcf/75B+np6fD09JTdR8+ePcPHH38Mb29vg9tZwc3NDV5eXnrfS46OjuByubVyL/3zzz948OCBwfdRcXExFi5ciA0bNmDw4MEIDg7GrFmzMGrUKKxbt07vuG3atJF9SUxJScHJkyeRlZWl8T5S9f6t771T058H2sTU597RFLMm7h1KdrTA4/HQpk0b2QqaChEREejUqVM9tUoZy7KYNWsW/vjjD5w7d65GPpxUvY6+3dq9e/dGTEwMoqKiZP9CQ0Mxbtw4REVFgcPhGNy+0tJSxMXFwc3NTe8YnTt3rrL88eHDh/Dy8jK0efjxxx/h7OyMQYMGGRSnqKgIJibKty+HwzF46XkFS0tLuLm5ITs7G6dOnUJYWJjBMRs3bgxXV1el+0gkEuHixYuvzX0EyN+sHz16hDNnzsDBwaFWXkffe2nChAmIjo5Wuo+EQiHmzZuHU6dO1Vj7srKykJSUpPe9xOPx0LZt21q5l3bu3Ik2bdroPeepglgshlgsrrV7ycbGBk5OTnj06BFu3ryp8j7S9P6t671TG58H2sTU9d7Rt5363Ds0jKWluXPnYsKECQgNDUXHjh2xfft2JCYmYsaMGXrFKygoQHx8vOzxkydPEBUVBXt7e3h6euocb+bMmdi7dy+OHDkCgUAg+wZgY2MDc3Nzvdq4cOFCDBw4EB4eHsjPz8e+fftw4cIFnDx5Uq94AoGgylispaUlHBwc9B5L/uSTTzB48GB4enoiPT0dX3zxBfLy8gwaypkzZw46deqElStXYuTIkbh+/Tq2b9+O7du36x0TKO/W//HHHzFp0iRwuYbdeoMHD8aKFSvg6emJgIAA3L59Gxs2bMCUKVMMinvq1CmwLIvmzZsjPj4e8+bNQ/PmzfGf//xHq+s1/V2Hh4dj5cqV8PX1ha+vL1auXAkLCwuMHTtWr3gvX75EYmKirJZHxQerq6urynpL6mIKhUK8++67iIyMxNGjRyGRSGT3kr29PXg8ns4xHRwcsGLFCgwZMgRubm7IysrCli1bkJycrLL0gKafu/KHiKmpKVxdXdG8efNq42mKaW9vj2XLluGdd96Bm5sbnj59ioULF8LR0RHDhg3TK6anpyfmzZuHUaNGoVu3bujZsydOnjyJv/76CxcuXNArHgDk5eXhwIEDWL9+vcp26RKze/fumDdvHszNzeHl5YWLFy9i9+7d2LBhg94xDxw4ACcnJ3h6eiImJgazZ8/G0KFDqyxyqaDp/buiHpm29442nwe63juaYpaVlel872iKWVhYqPO9o5JOa7fecN9++y3r5eXF8ng8tnXr1gYt6z5//jwLoMq/SZMm6RWvulgA2B9//FHvNk6ZMkX28zo5ObG9e/dmT58+rXe86hi69HzUqFGsm5sba2pqygqFQnb48OFsbGyswe3666+/2MDAQNbMzIz18/Njt2/fbnDMU6dOsQDYBw8eGBwrLy+PnT17Nuvp6cny+Xy2SZMm7KJFi9jS0lKD4u7fv59t0qQJy+PxWFdXV3bmzJlsTk6O1tdr+ruWSqXs0qVLWVdXV9bMzIzt1q0bGxMTo3e8H3/8sdrnly5dqlfMimW41f07f/68XjGLi4vZYcOGsUKhkOXxeKybmxs7ZMgQ9vr163r/3JVps/RcXcyioiK2X79+rJOTE2tqasp6enqykyZNYhMTE/WOWWHnzp2sj48Py+fz2ZCQEPbw4cMGxdu2bRtrbm6u9d+lppgpKSns5MmTWaFQyPL5fLZ58+bs+vXr1ZaF0BRz06ZNrLu7u+x3uXjxYrX3pjbv37rcO9rE0/Xe0RRTn3tHU0x97h1VmFcvSAghhBDSINGcHUIIIYQ0aJTsEEIIIaRBo2SHEEIIIQ0aJTuEEEIIadAo2SGEEEJIg0bJDiGEEEIaNEp2CCGEENKgUbJDCDEKPXr0QHh4eH03gxBihCjZIYQQQkiDRskOIYSoIBKJ6rsJhJAaQMkOIcRoSKVSfPrpp7C3t4erqyuWLVsmey4xMRFhYWGwsrKCtbU1Ro4cibS0NNnzkydPxtChQ5XihYeHo0ePHrLHPXr0wKxZszB37lw4Ojqib9++tfwTEULqAiU7hBCjsWvXLlhaWuLatWv48ssvsXz5ckRERIBlWQwdOhQvX77ExYsXERERgcePH2PUqFF6vQaXy8Xly5exbdu2WvgpCCF1jVvfDSCEEG0FBwdj6dKlAABfX19s3rwZZ8+eBQBER0fjyZMn8PDwAAD8/PPPCAgIwI0bN9C2bVutX8PHxwdffvllzTeeEFJvqGeHEGI0goODlR67ubkhPT0dcXFx8PDwkCU6AODv7w9bW1vExcXp9BqhoaE10lZCyOuDkh1CiNEwNTVVeswwDKRSKViWBcMwVc5XPG5iYgKWZZWeF4vFVa6xtLSswRYTQl4HlOwQQoyev78/EhMTkZSUJDt279495ObmokWLFgAAJycnpKSkKF0XFRVVl80khNQTSnYIIUavT58+CA4Oxrhx4xAZGYnr169j4sSJ6N69u2xYqlevXrh58yZ2796NR48eYenSpbh79249t5wQUhco2SGEGD2GYXD48GHY2dmhW7du6NOnD5o0aYL9+/fLzunfvz+WLFmCTz/9FG3btkV+fj4mTpxYj60mhNQVhq08iE0IIYQQ0oBQzw4hhBBCGjRKdgghhBDSoFGyQwghhJAGjZIdQgghhDRolOwQQgghpEGjZIcQQgghDRolO4QQQghp0CjZIYQQQkiDRskOIYQQQho0SnYIIYQQ0qBRskMIIYSQBo2SHUIIIYQ0aP8PQjeyZTTt9JsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ##折线图(连接起来的散点图)图形绘制  横坐标 是 hour，纵坐标是 count,不同的季节 分开绘制\n",
    "sns.pointplot(data=df6,x='hour',y='count',hue='season')\n",
    "plt.show()\n",
    "# sns.pointplot(data=df6,x='hour',y='count',hue='season',join=False)\n",
    "# plt.show()\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0eb087a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{('Friday', 0): [4582, 7311, 501, 1898, 8271, 10574, 6689, 5708, 1323, 9783, 3959, 3792, 666, 4367, 4918, 10334, 9207, 8655, 829, 301, 2618, 3242, 9615, 1658, 6354, 6090, 4990, 949, 6904, 2858, 9375, 4199, 2282, 6522, 5923, 7695, 10166, 9951, 1113, 2690, 2450, 138, 3410, 5541, 1276, 5326, 8031, 10742, 1491, 6258, 9999, 5158, 4750, 2066, 7863, 7071, 8439, 3026, 3626, 7479, 8991, 8823, 7647, 3578], ('Friday', 1): [9616, 3793, 7312, 2283, 9208, 9952, 3627, 950, 139, 8032, 10335, 2451, 8272, 4368, 3027, 7696, 8440, 1114, 6905, 5542, 5924, 4751, 3411, 830, 4919, 1324, 2067, 6091, 4991, 3243, 5327, 6355, 1659, 2691, 7648, 2859, 3960, 2619, 7480, 502, 6259, 8992, 1899, 10000, 3579, 10743, 7864, 8656, 6523, 1492, 1277, 9376, 6690, 4200, 9784, 667, 8824, 5709, 4583, 10167, 302, 10575, 5159, 7072], ('Friday', 2): [831, 2860, 2692, 4369, 5543, 668, 2452, 10168, 3580, 10001, 3794, 10744, 503, 7313, 5160, 3628, 7649, 4920, 9785, 8441, 8657, 8273, 9953, 10336, 4584, 6691, 1900, 3244, 2620, 1278, 4201, 8033, 2068, 6260, 6906, 5925, 4752, 4992, 8825, 9209, 6356, 2284, 7481, 9617, 1493, 6092, 7865, 7073, 8993, 3412, 5328, 7697, 9377, 3028, 303, 1115, 140, 5710, 6524, 1325, 1660, 951, 3961, 10576], ('Friday', 3): [3245, 10169, 2861, 9618, 3795, 3629, 6093, 7650, 2453, 10002, 4921, 7698, 10577, 8274, 4585, 10745, 8994, 2693, 8826, 3413, 8658, 10337, 8034, 2069, 3029, 6692, 952, 4993, 6261, 9786, 5926, 4202, 9954, 504, 5161, 5544, 3962, 9378, 304, 7314, 1494, 1279, 2621, 6357, 4753, 1116, 8442, 2285, 4370, 1901, 7074, 7482, 5711, 832, 9210, 1326, 3581, 7866, 1661, 5329, 6525, 6907], ('Friday', 4): [1902, 2286, 3246, 2622, 7483, 9211, 3582, 7699, 8827, 5162, 8443, 6526, 6693, 833, 1662, 8275, 5545, 10338, 10003, 5712, 141, 1495, 3963, 9787, 6262, 6908, 1327, 2070, 2694, 9619, 4371, 4922, 5927, 7651, 10170, 7867, 7315, 3030, 4994, 8995, 3796, 9379, 8035, 10746, 3414, 9955, 7075, 6358, 2862, 953, 6094, 4754, 4586, 2454, 10578, 3630, 5330, 4203, 8659], ('Friday', 5): [10747, 6095, 5331, 6359, 2071, 1496, 8996, 7076, 7484, 7652, 4204, 2455, 4923, 8828, 954, 5546, 1328, 4587, 7700, 1663, 5713, 5163, 5928, 10171, 3247, 9212, 6527, 3415, 10339, 7868, 669, 834, 1280, 8276, 8660, 4372, 6694, 9956, 3631, 9620, 2863, 1903, 6263, 8036, 10579, 2287, 3964, 2623, 1117, 8444, 6909, 3583, 9788, 7316, 505, 2695, 305, 4755, 9380, 142, 4995, 10004, 3031, 3797], ('Friday', 6): [4588, 4373, 2288, 6528, 2624, 7317, 5929, 7701, 6910, 7653, 3416, 4205, 835, 8445, 4924, 10340, 9789, 306, 1664, 1329, 5332, 4996, 8829, 9381, 7869, 10005, 8997, 2864, 8661, 2072, 3032, 1497, 3632, 10172, 8037, 1281, 7485, 5164, 2696, 506, 7077, 3584, 3798, 9957, 9621, 3248, 10580, 4756, 10748, 9213, 6695, 2456, 5547, 3965, 6360, 8277, 143, 6096, 670, 1904, 5714, 1118, 955, 6264], ('Friday', 7): [1119, 9958, 3799, 7702, 307, 9622, 4925, 3966, 956, 6529, 8278, 3417, 1330, 1282, 3633, 4206, 8998, 6696, 6265, 4757, 836, 2697, 2073, 2865, 8446, 4374, 5715, 6911, 7870, 6097, 9214, 10749, 507, 5165, 10581, 7654, 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8091, 7539, 8883, 3852, 723, 7371, 360, 4019, 6797, 6414, 10466, 6581, 4427, 5601, 3086, 5218, 2918, 4810, 6150, 2342, 10634, 6749, 5434, 1171, 5050, 7923, 9843, 2510, 8715, 10226, 8331, 10059, 3302, 9435, 10802, 1958, 4259, 9675, 4642, 1718, 8163, 5983, 2678, 2126, 1008, 6964, 2750, 197, 1383, 3470, 9099, 5768, 10394, 1551, 5386, 8499, 9051, 35, 7131, 4091, 560], ('Sunday', 13): [3471, 8500, 8092, 724, 6415, 10227, 6965, 2127, 1384, 4643, 8884, 1552, 1719, 5984, 7924, 2919, 198, 2679, 9100, 4260, 4428, 5051, 6582, 3853, 9844, 5219, 1009, 10060, 9676, 5435, 1172, 5387, 36, 10467, 10803, 9436, 2343, 3303, 7540, 6151, 2511, 7132, 6750, 9268, 10635, 10395, 3087, 7756, 8332, 5602, 5769, 3687, 6319, 9052, 8716, 1791, 4811, 8164, 4020, 361, 4092, 1959, 561, 7372, 6798, 2751], ('Sunday', 14): [10468, 362, 1792, 7757, 2680, 10396, 6416, 10636, 6966, 7925, 4812, 6751, 9053, 7373, 1720, 6152, 8501, 3688, 37, 5603, 10804, 10061, 2752, 1960, 4644, 8717, 6320, 4093, 7541, 5388, 8333, 3472, 1385, 3304, 3854, 199, 9677, 6583, 562, 8885, 2512, 4429, 2128, 1173, 5220, 3088, 5770, 2920, 1553, 4021, 7133, 5436, 5052, 9101, 725, 2344, 1010, 4261, 9845, 8165, 8093, 9437, 5985, 6799, 9269, 10228], ('Sunday', 15): [1386, 9054, 10229, 9270, 3305, 5986, 10469, 5221, 4813, 9438, 363, 4645, 2345, 7374, 38, 1961, 6967, 10397, 726, 3689, 6417, 2753, 8166, 6584, 3855, 10805, 8094, 6752, 8502, 3089, 8718, 9678, 2513, 9846, 200, 8334, 5389, 7134, 5771, 1011, 4262, 4022, 6321, 3473, 2681, 1554, 5437, 5604, 4094, 10062, 8886, 7542, 2921, 6153, 4430, 7758, 6800, 2129, 9102, 5053, 7926, 1721, 1793, 1174, 563, 10637], ('Sunday', 16): [7135, 1722, 7759, 564, 2922, 6753, 7927, 2130, 4023, 5772, 6322, 4814, 6968, 3306, 2754, 4095, 1012, 6418, 5054, 5390, 3474, 3690, 9439, 9679, 10230, 7375, 5438, 9055, 4431, 727, 4646, 2682, 6585, 9271, 8503, 10470, 6154, 1962, 1794, 1555, 1387, 8719, 2346, 4263, 5987, 10638, 9103, 8887, 39, 5605, 5222, 364, 3090, 10398, 10063, 9847, 8167, 3856, 6801, 8335, 7543, 8095, 10806, 201, 2514, 1175], ('Sunday', 17): [6969, 10399, 2923, 7376, 6419, 5391, 6754, 9056, 10064, 5055, 4096, 7544, 3091, 3691, 2683, 4024, 5223, 2515, 202, 7760, 8336, 5439, 6155, 365, 10807, 9272, 2347, 1176, 6802, 9104, 3475, 4647, 2755, 8096, 9848, 9440, 728, 1556, 5773, 1723, 3857, 4432, 40, 8720, 10231, 8168, 4264, 9680, 6586, 7928, 7136, 10471, 1013, 8504, 4815, 5606, 6323, 2131, 1795, 5988, 1388, 565, 10639, 8888, 3307, 1963], ('Sunday', 18): [4816, 7761, 9441, 6755, 6156, 3308, 3476, 8889, 5056, 366, 6970, 3858, 10808, 7545, 1796, 1177, 5774, 7929, 5392, 5989, 7137, 2132, 6420, 2756, 1014, 7377, 4025, 1389, 5224, 4097, 1557, 6324, 10472, 203, 2348, 41, 6587, 10400, 8337, 6803, 2924, 9057, 4648, 2516, 10065, 8721, 3092, 10232, 9849, 1724, 8505, 4433, 9681, 8169, 1964, 9273, 566, 2684, 8097, 3692, 9105, 5440, 4265, 10640, 5607, 729], ('Sunday', 19): [9274, 7138, 1965, 2685, 9682, 6804, 5393, 5225, 4098, 1797, 8338, 9442, 2133, 8170, 8098, 8506, 10066, 5057, 567, 4026, 1178, 4649, 9058, 8722, 1725, 3093, 1390, 7762, 4817, 3859, 10233, 6421, 5775, 10641, 204, 6756, 7378, 10809, 1015, 7546, 2349, 4266, 6971, 3477, 367, 5990, 2757, 3309, 2925, 9850, 10401, 1558, 6325, 5608, 8890, 9106, 730, 4434, 7930, 5441, 3693, 6588, 42, 10473, 2517, 6157], ('Sunday', 20): [731, 6158, 2926, 3094, 2686, 7547, 4027, 2758, 9059, 3694, 8099, 5394, 6757, 205, 7379, 4650, 2518, 6805, 1559, 6972, 3478, 8723, 9275, 9107, 9443, 6422, 5058, 10642, 5226, 6326, 4099, 10810, 9683, 1726, 1798, 2134, 5991, 7139, 368, 8171, 8507, 7931, 1016, 10402, 4818, 4435, 10474, 2350, 3310, 1391, 3860, 4267, 8339, 9851, 5609, 1179, 10067, 7763, 6589, 5776, 43, 1966, 8891, 5442, 10234, 568], ('Sunday', 21): [4651, 10475, 6973, 3095, 10811, 8100, 10235, 2351, 9444, 10643, 569, 7140, 5777, 1967, 6159, 4028, 732, 3695, 8892, 9108, 5610, 1799, 5992, 5443, 6806, 5395, 206, 2759, 8508, 5059, 4100, 9684, 9060, 2687, 10068, 7932, 2927, 7764, 5227, 1392, 6590, 1560, 44, 3479, 1180, 2135, 8172, 6758, 4268, 1727, 8724, 4819, 3311, 7380, 3861, 1017, 8340, 10403, 7548, 369, 4436, 9852, 2519, 6327, 6423, 9276], ('Sunday', 22): [5444, 3480, 3312, 5611, 2136, 2688, 9685, 1968, 4652, 6424, 8509, 10644, 6974, 8893, 45, 6759, 4101, 10404, 733, 9853, 2520, 6807, 8725, 370, 9109, 5778, 7141, 10069, 6591, 8101, 7381, 570, 4269, 4820, 207, 6160, 3096, 1018, 9445, 2760, 2928, 4029, 3696, 1728, 10236, 1393, 1181, 1800, 5060, 9277, 6328, 3862, 10812, 8341, 7765, 5993, 7549, 1561, 8173, 2352, 5228, 5396, 10476, 9061, 7933, 4437], ('Sunday', 23): [7142, 1729, 2761, 3481, 5397, 8894, 10813, 6425, 4270, 5229, 3313, 4653, 2137, 3097, 1801, 10237, 8174, 5779, 2929, 6592, 2521, 371, 9278, 2689, 6329, 4102, 7934, 571, 6975, 46, 10477, 1182, 5994, 9686, 3697, 4438, 9446, 9110, 7382, 4821, 10405, 3863, 7766, 6161, 9062, 4030, 1969, 8342, 8726, 208, 1394, 5445, 7550, 5061, 5612, 2353, 8102, 1562, 6808, 6760, 1019, 734, 8510, 9854, 10645, 10070], ('Thursday', 0): [7839, 2834, 6665, 8967, 4343, 478, 3602, 5517, 2426, 2042, 1467, 4966, 1091, 7047, 7287, 277, 7455, 6498, 6234, 2210, 9975, 3002, 7623, 5851, 4726, 10550, 10310, 8799, 9351, 8007, 10718, 9591, 4894, 3935, 2594, 8415, 9927, 1252, 925, 9183, 8583, 5302, 1634, 3386, 6330, 805, 115, 5899, 8247, 6880, 5684, 3218, 9759, 643, 3554, 3769, 8631, 7215, 4175, 2258, 10143, 1874, 6066, 5134, 4558], ('Thursday', 1): [7624, 3936, 5852, 7048, 278, 8632, 806, 1875, 8968, 3555, 1253, 2595, 6881, 2427, 8800, 5685, 8248, 9760, 4176, 10551, 8584, 9592, 6235, 479, 2211, 5135, 4967, 9976, 6499, 1092, 3387, 1635, 10719, 5518, 5900, 3003, 6331, 2043, 7456, 7840, 9352, 644, 3770, 1468, 10311, 6666, 4559, 9184, 6067, 4727, 9928, 7216, 2835, 5303, 7288, 926, 8416, 8008, 116, 4344, 3603, 2259, 4895, 10144, 3219], ('Thursday', 2): [7841, 645, 9593, 1093, 3388, 4177, 3604, 9353, 5686, 8009, 3004, 6667, 117, 2212, 6236, 5304, 9977, 2044, 3220, 8417, 10720, 807, 8969, 4560, 9929, 7289, 6882, 6332, 8249, 4968, 2428, 9761, 9185, 5519, 1469, 7217, 7625, 10145, 5853, 1876, 2596, 6068, 10312, 4896, 3556, 7049, 10552, 3937, 4345, 8585, 6500, 5901, 4728, 480, 2260, 279, 927, 5136, 1254, 1636, 8801, 8633, 2836, 7457], ('Thursday', 3): [6333, 2261, 5854, 5305, 1637, 2597, 3771, 8634, 10553, 5137, 481, 9594, 9978, 6883, 7842, 8586, 808, 7626, 8802, 928, 8970, 5520, 10721, 3605, 4178, 3557, 7458, 7218, 8010, 7290, 9354, 6069, 2045, 4897, 9186, 1470, 7050, 2837, 3938, 8418, 3389, 4969, 2213, 5902, 4561, 3005, 2429, 3221, 9762, 6668, 5687, 9930, 6501, 4729, 8250, 1255, 4346, 280, 6237, 1877, 10313], ...}\n"
     ]
    }
   ],
   "source": [
    "# #查看星期几weekday和小时hour对用车数量count的影响\n",
    "g=all_df.groupby(['weekday','hour'],sort=True)\n",
    "print(g.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9290ef29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s5是双列 行索引\n",
      "weekday    hour\n",
      "Friday     0        53.234375\n",
      "           1        24.453125\n",
      "           2        12.531250\n",
      "           3         6.322581\n",
      "           4         5.932203\n",
      "                      ...    \n",
      "Wednesday  19      348.230769\n",
      "           20      251.569231\n",
      "           21      190.661538\n",
      "           22      140.400000\n",
      "           23       80.138462\n",
      "Name: count, Length: 168, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#组内求均值\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "df5 = g.apply(mean_numeric_columns)  # 对每个分组应用函数计算均值\n",
    "# df5=g.agg(np.mean)\n",
    "s5=df5['count']\n",
    "print(\"s5是双列 行索引\")\n",
    "print(s5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0f931e37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "重置索引后的数据帧df6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "      <td>53.234375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Friday</td>\n",
       "      <td>1</td>\n",
       "      <td>24.453125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Friday</td>\n",
       "      <td>2</td>\n",
       "      <td>12.531250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Friday</td>\n",
       "      <td>3</td>\n",
       "      <td>6.322581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>5.932203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>19</td>\n",
       "      <td>348.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>20</td>\n",
       "      <td>251.569231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>21</td>\n",
       "      <td>190.661538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>22</td>\n",
       "      <td>140.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>23</td>\n",
       "      <td>80.138462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       weekday  hour       count\n",
       "0       Friday     0   53.234375\n",
       "1       Friday     1   24.453125\n",
       "2       Friday     2   12.531250\n",
       "3       Friday     3    6.322581\n",
       "4       Friday     4    5.932203\n",
       "..         ...   ...         ...\n",
       "163  Wednesday    19  348.230769\n",
       "164  Wednesday    20  251.569231\n",
       "165  Wednesday    21  190.661538\n",
       "166  Wednesday    22  140.400000\n",
       "167  Wednesday    23   80.138462\n",
       "\n",
       "[168 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #重置索引 使得 原来的双索引，变成 属性 列\n",
    "df6=s5.reset_index()\n",
    "print(\"重置索引后的数据帧df6\")\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "37d98f41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ##折线图(连接起来的散点图)图形绘制  横坐标 是 hour，纵坐标是 count,不同的星期几 分开绘制\n",
    "sns.pointplot(data=df6,x='hour',y='count',hue='weekday')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1f51151a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #根据上图 周末和平时的用车高峰地图不同，新增属性 hour_weekday_section\n",
    "# #将 小时 和 星期几 结合起来的 时间段 作为一个 新的属性\n",
    "def f(hour,weekday):\n",
    "    if weekday not in ['Saturday', 'Sunday']:\n",
    "        if hour >= 0 and hour <= 6:\n",
    "            return 0\n",
    "        elif hour >= 7 and hour <= 10:\n",
    "            return 1\n",
    "        elif hour >= 11 and hour <= 15:\n",
    "            return 2\n",
    "        elif hour >= 16 and hour <= 20:\n",
    "            return 3\n",
    "        else:\n",
    "            return 4\n",
    "    else:  # 周六日用车分析\n",
    "        if hour >= 0 and hour <= 8:\n",
    "            return 5\n",
    "        elif hour >= 9 and hour <= 20:\n",
    "            return 6\n",
    "        else:\n",
    "            return 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e019f9fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_df['hour_weekday_section'].value_counts()\n",
      "hour_weekday_section\n",
      "0    2220\n",
      "2    1619\n",
      "6    1581\n",
      "3    1496\n",
      "1    1272\n",
      "5    1183\n",
      "4     972\n",
      "7     396\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 2）对 整个 数据帧 df.apply(lamda x:)\n",
    "# 此时 x 是 df 这个数据帧 的一个 子元素\n",
    "# (数据帧的子元素 或者 是 一行（一个样本），或者 一列(一个属性列))\n",
    "# 如何确定一行还是一列 （ axis）\n",
    "#  df.apply(lamda x:   axis=1)\n",
    "all_df['hour_weekday_section']=all_df.apply(lambda row: f(row['hour'],row['weekday']),axis=1)\n",
    "print(\"all_df['hour_weekday_section'].value_counts()\")\n",
    "print(all_df['hour_weekday_section'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "deeafa96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #\n",
    "#查看假期和 非假期 样本数量\n",
    "all_df['holiday'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f733166c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #查看非假期和假期的用车均值\n",
    "#组内求均值\n",
    "import pandas as pd\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "all_df.groupby('holiday').apply(mean_numeric_columns)['count'].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b08f9b58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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9124], (1, 14): [2776, 386, 5794, 9701, 10252, 8237, 4764, 1672, 7157, 9125, 4285, 3712, 5459], (1, 15): [1673, 2777, 4765, 5460, 387, 8238, 4286, 7158, 9702, 3713, 5795, 9126, 10253], (1, 16): [7159, 388, 2778, 1674, 5796, 3714, 8239, 9127, 9703, 4766, 10254, 4287, 5461], (1, 17): [1675, 9128, 5462, 5797, 4288, 4767, 10255, 8240, 7160, 2779, 389, 9704, 3715], (1, 18): [390, 8241, 2780, 9705, 4289, 3716, 10256, 4768, 5463, 9129, 5798, 1676, 7161], (1, 19): [3717, 8242, 4769, 4290, 1677, 9130, 10257, 391, 5799, 5464, 2781, 9706, 7162], (1, 20): [4291, 8243, 4770, 9707, 9131, 10258, 5465, 5800, 392, 1678, 7163, 2782, 3718], (1, 21): [9132, 4292, 10259, 5801, 8244, 7164, 393, 9708, 5466, 2783, 3719, 1679, 4771], (1, 22): [10260, 2784, 1680, 4293, 5467, 394, 4772, 9709, 3720, 5802, 7165, 8245, 9133], (1, 23): [7166, 9710, 395, 2785, 5468, 4773, 3721, 5803, 9134, 10261, 1681, 4294, 8246]}\n"
     ]
    }
   ],
   "source": [
    "#考虑 是否是 节假日 和 小时 双因素，对用车数量的影响\n",
    "g=all_df.groupby(['holiday','hour'],sort=True)\n",
    "print(g.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b485e337",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s5是双列 行索引\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "holiday  hour\n",
       "0        0        54.796380\n",
       "         1        33.582766\n",
       "         2        22.747126\n",
       "         3        11.738717\n",
       "         4         6.377622\n",
       "         5        19.961276\n",
       "         6        77.357466\n",
       "         7       216.119910\n",
       "         8       347.253555\n",
       "         9       222.088235\n",
       "         10      173.461538\n",
       "         11      208.787330\n",
       "         12      252.446712\n",
       "         13      255.774266\n",
       "         14      241.164786\n",
       "         15      253.169300\n",
       "         16      315.361991\n",
       "         17      407.566489\n",
       "         18      377.496124\n",
       "         19      315.341629\n",
       "         20      228.792325\n",
       "         21      173.121896\n",
       "         22      133.347630\n",
       "         23       90.009029\n",
       "1        0        66.769231\n",
       "         1        43.230769\n",
       "         2        28.000000\n",
       "         3        12.416667\n",
       "         4         7.384615\n",
       "         5        13.230769\n",
       "         6        38.923077\n",
       "         7       111.000000\n",
       "         8       229.000000\n",
       "         9       211.307692\n",
       "         10      230.538462\n",
       "         11      274.846154\n",
       "         12      318.384615\n",
       "         13      326.384615\n",
       "         14      321.076923\n",
       "         15      292.769231\n",
       "         16      314.846154\n",
       "         17      368.000000\n",
       "         18      344.538462\n",
       "         19      280.230769\n",
       "         20      219.153846\n",
       "         21      181.846154\n",
       "         22      141.384615\n",
       "         23       72.461538\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#组内求均值\n",
    "import pandas as pd\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "df5 = g.apply(mean_numeric_columns)  # 对每个分组应用函数计算均值\n",
    "s5=df5['count']\n",
    "print(\"s5是双列 行索引\")\n",
    "s5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2a9352f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "重置索引后的数据帧df6\n",
      "    holiday  hour       count\n",
      "0         0     0   54.796380\n",
      "1         0     1   33.582766\n",
      "2         0     2   22.747126\n",
      "3         0     3   11.738717\n",
      "4         0     4    6.377622\n",
      "5         0     5   19.961276\n",
      "6         0     6   77.357466\n",
      "7         0     7  216.119910\n",
      "8         0     8  347.253555\n",
      "9         0     9  222.088235\n",
      "10        0    10  173.461538\n",
      "11        0    11  208.787330\n",
      "12        0    12  252.446712\n",
      "13        0    13  255.774266\n",
      "14        0    14  241.164786\n",
      "15        0    15  253.169300\n",
      "16        0    16  315.361991\n",
      "17        0    17  407.566489\n",
      "18        0    18  377.496124\n",
      "19        0    19  315.341629\n",
      "20        0    20  228.792325\n",
      "21        0    21  173.121896\n",
      "22        0    22  133.347630\n",
      "23        0    23   90.009029\n",
      "24        1     0   66.769231\n",
      "25        1     1   43.230769\n",
      "26        1     2   28.000000\n",
      "27        1     3   12.416667\n",
      "28        1     4    7.384615\n",
      "29        1     5   13.230769\n",
      "30        1     6   38.923077\n",
      "31        1     7  111.000000\n",
      "32        1     8  229.000000\n",
      "33        1     9  211.307692\n",
      "34        1    10  230.538462\n",
      "35        1    11  274.846154\n",
      "36        1    12  318.384615\n",
      "37        1    13  326.384615\n",
      "38        1    14  321.076923\n",
      "39        1    15  292.769231\n",
      "40        1    16  314.846154\n",
      "41        1    17  368.000000\n",
      "42        1    18  344.538462\n",
      "43        1    19  280.230769\n",
      "44        1    20  219.153846\n",
      "45        1    21  181.846154\n",
      "46        1    22  141.384615\n",
      "47        1    23   72.461538\n"
     ]
    }
   ],
   "source": [
    "#重置索引 使得 原来的双索引，变成 属性 列\n",
    "df6=s5.reset_index()\n",
    "print(\"重置索引后的数据帧df6\")\n",
    "print(df6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e12ff668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##折线图(连接起来的散点图)图形绘制  横坐标 是 hour，纵坐标是 count,是否是节假日 分开绘制\n",
    "sns.pointplot(data=df6,x='hour',y='count',hue='holiday')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "239cc3de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #--------------------------------------------------#\n",
    "#查看工作日和 非工作日 样本数量\n",
    "all_df['workingday'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8051025b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看工作日和 非工作日 的用车均值\n",
    "import pandas as pd\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "all_df.groupby('workingday').apply(mean_numeric_columns)['count'].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b6ee0070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(0, 0): [997, 0, 5971, 8151, 5947, 2642, 9807, 6952, 5206, 348, 10382, 6306, 9831, 1136, 2114, 548, 7911, 9423, 9111, 1371, 4223, 9063, 6785, 6713, 9231, 6282, 10023, 688, 9255, 1515, 3674, 3840, 2306, 2666, 324, 2738, 6737, 6114, 9399, 5732, 6570, 7719, 6138, 7887, 1539, 6546, 4007, 8295, 2330, 7119, 1658, 3698, 8703, 10454, 2906, 1778, 1946, 10766, 4798, 2762, 6928, 5565, 3983, 10790, 5589, 8079, 5422, 6378, 8487, 10622, 4247, 4391, 2498, 5374, 7095, 10190, 1682, 8055, 4079, 7143, 5182, 10238, 9087, 1299, 973, 524, 10214, 24, 1922, 1160, 853, 3434, 5014, 10430, 3816, 7335, 3290, 9687, 8223, 5446, ...], (0, 1): [349, 5972, 9640, 5757, 9256, 9064, 6307, 1779, 9088, 5948, 7120, 1137, 1540, 5039, 9664, 1372, 8872, 1707, 7504, 4607, 3984, 10239, 2739, 6786, 9016, 5015, 8080, 7336, 2499, 2475, 4775, 1, 974, 10623, 3291, 9112, 10359, 9400, 186, 5351, 9688, 713, 5447, 10024, 1683, 2715, 9232, 2907, 6929, 10191, 7096, 4751, 10791, 3267, 5566, 4392, 4272, 7528, 8488, 1348, 5183, 5733, 3075, 2763, 4080, 6139, 5423, 2643, 3675, 8704, 6547, 2883, 7720, 9424, 1161, 6283, 9040, 2091, 3699, 10455, 1659, 4799, 4224, 10383, 7888, 3651, 10599, 3051, 7360, 8680, 549, 2115, 6571, 2331, 6115, 6953, 7672, 3817, 325, 1947, ...], (0, 2): [714, 3436, 1541, 3460, 7529, 6308, 10240, 10768, 2716, 9689, 5424, 4393, 2908, 6116, 8489, 26, 7673, 9425, 5949, 10600, 2740, 9065, 8681, 10025, 3268, 4225, 6380, 2884, 7097, 1301, 10792, 6787, 3985, 1517, 2500, 3700, 7745, 1373, 5758, 2764, 5591, 5782, 163, 5040, 7337, 9113, 999, 5448, 2308, 9041, 4632, 8057, 8873, 2092, 5208, 10049, 6930, 7721, 8081, 9089, 10192, 9017, 9233, 4944, 1138, 10384, 8153, 6284, 187, 4752, 9641, 2116, 9401, 4608, 10216, 8849, 3842, 8297, 7145, 6954, 4417, 2644, 4009, 3818, 5352, 1924, 3052, 7889, 8705, 7121, 8225, 6404, 5016, 8465, 6739, 2668, 1780, 10624, 2, 1684, ...], (0, 3): [6285, 9258, 691, 1542, 4945, 1374, 4609, 1302, 5041, 9114, 2117, 1781, 2501, 27, 4058, 5759, 375, 715, 8850, 2333, 2093, 9426, 7674, 10361, 1139, 8874, 527, 10217, 10601, 6141, 10193, 10433, 9402, 6740, 7098, 4801, 856, 6788, 8154, 2909, 10457, 2885, 3461, 551, 5017, 4250, 3819, 9018, 5353, 2309, 3269, 5425, 9666, 6549, 8682, 8490, 7146, 10241, 6931, 10385, 4633, 7506, 1949, 9642, 7362, 7722, 10769, 3986, 188, 6381, 5950, 6117, 1925, 9234, 1162, 8226, 976, 2669, 3653, 8082, 4394, 6572, 5568, 7890, 1350, 2645, 6955, 2765, 4777, 3053, 4753, 9066, 164, 5209, 9042, 7530, 10625, 7746, 3843, 3437, ...], (0, 4): [5426, 9019, 10194, 4778, 4083, 328, 3294, 4275, 9835, 1686, 3654, 7891, 1950, 6932, 10027, 7339, 1351, 9811, 528, 10362, 552, 4011, 1662, 28, 7147, 6310, 5186, 1543, 9643, 2886, 8707, 977, 10434, 8059, 10242, 1926, 8683, 8083, 2334, 6789, 3438, 9403, 5784, 9667, 1519, 5736, 3078, 6717, 4946, 4419, 10218, 2766, 3987, 8467, 4227, 352, 7531, 1140, 6741, 6550, 3820, 10770, 8155, 692, 376, 3678, 4, 2910, 1710, 2118, 7099, 2094, 716, 7507, 7675, 3462, 10794, 2718, 1163, 6286, 5354, 4610, 1303, 10386, 7123, 10626, 2310, 189, 3270, 8851, 6406, 2502, 4634, 8323, 7723, 9067, 5210, 5449, 165, 4251, ...], (0, 5): [9692, 9044, 3703, 5211, 7724, 5187, 5450, 6311, 3439, 6742, 1304, 5379, 8468, 10363, 1520, 4084, 5355, 1376, 353, 3679, 5737, 10028, 2479, 8876, 9092, 10459, 5, 4252, 7148, 8492, 5785, 10052, 10795, 7892, 9068, 9020, 4803, 1663, 3655, 3271, 8852, 693, 9428, 9404, 5976, 10435, 9668, 3463, 2503, 9116, 6790, 4779, 10771, 1951, 6718, 6551, 4420, 329, 8324, 10195, 2119, 9644, 858, 2647, 6574, 8708, 2671, 3821, 9236, 10387, 6383, 1141, 1711, 2743, 190, 8156, 6119, 5427, 6287, 6407, 5594, 8228, 7532, 1687, 166, 5570, 3055, 4947, 5761, 7676, 7124, 3988, 7100, 3845, 1927, 1544, 3295, 10219, 529, 2311, ...], (0, 6): [6552, 5762, 8061, 2120, 29, 7749, 2504, 354, 10364, 1784, 10220, 5571, 4948, 5356, 2720, 7725, 8325, 4229, 4013, 6312, 9813, 6288, 5595, 330, 3464, 6934, 1545, 5977, 10053, 1712, 5738, 9669, 10796, 3704, 3056, 2480, 10244, 9405, 9021, 167, 5380, 6384, 5428, 8685, 7101, 5451, 9093, 2744, 7125, 8853, 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672, 2938, 308, 10294, 4303, 2602, 3034, 508, 3634, 4926, 1190, 8375, 7823, 1642, 10726, 4518, 4039, 1051, 9191, 6816, 6242, 3250, 1451, 4159, 5406, 8807, 2170, 7079, 8519, 2002, 53, 9479, 7391, 285, 7871, 4590, 5644, 5142, 3226, 3871, 7775, 604, 3130, 909, 8351, 6697, ...], (1, 9): [2051, 4591, 2627, 6507, 439, 7608, 5908, 1052, 2963, 123, 240, 1028, 9744, 2195, 8808, 6698, 3872, 262, 5311, 3896, 605, 2027, 4879, 8520, 9936, 6435, 5502, 3347, 2843, 9336, 2555, 7584, 2699, 5622, 9504, 4328, 10847, 958, 8280, 8256, 4136, 1191, 2003, 9384, 1237, 4999, 3587, 8040, 2387, 9528, 6674, 3107, 1332, 7824, 10511, 3944, 3011, 6195, 1883, 10655, 10559, 2579, 146, 6913, 8448, 10823, 10535, 8760, 7032, 6363, 7872, 8376, 1571, 790, 1763, 6267, 100, 5645, 1214, 2867, 5167, 10871, 218, 3395, 77, 7320, 9768, 6770, 7704, 8904, 5860, 9144, 8352, 3777, 6027, 9600, 3491, 4735, 4855, 1098, ...], (1, 10): [3612, 6866, 6268, 10416, 3778, 124, 8425, 9793, 9169, 6052, 959, 7609, 7849, 3252, 9505, 911, 3564, 4880, 55, 10752, 4377, 241, 1908, 5000, 7489, 7010, 768, 3420, 8521, 5408, 5861, 9745, 2532, 8017, 10512, 7801, 2364, 263, 4185, 10129, 6436, 2388, 8761, 4161, 3156, 1238, 1740, 9553, 5933, 6986, 4928, 1812, 1122, 7633, 2604, 3012, 9865, 10488, 9577, 6244, 9337, 935, 7297, 7993, 3396, 2844, 310, 10824, 6028, 2220, 5072, 10704, 10105, 5909, 9361, 7585, 8353, 9313, 7057, 8569, 1215, 9001, 5813, 606, 2292, 7873, 3132, 8833, 2868, 10176, 8185, 2244, 10680, 10320, 7081, 8737, 815, 1980, 1572, 6842, ...], (1, 11): [5719, 8138, 3874, 6509, 1100, 5695, 1263, 4138, 6533, 2797, 5504, 4042, 6101, 3493, 5886, 4881, 7610, 2053, 7586, 4593, 10177, 5337, 7442, 10417, 7418, 10153, 6700, 769, 9962, 8594, 7250, 8546, 4857, 6652, 1597, 4210, 4665, 3109, 5552, 8642, 8210, 8786, 7994, 102, 5624, 10345, 3922, 1078, 8018, 220, 5145, 792, 9722, 9002, 8522, 9986, 7058, 10082, 2197, 5934, 7202, 2029, 2701, 488, 5313, 6341, 5097, 1885, 630, 9194, 8114, 1054, 2293, 9626, 1216, 6819, 9458, 5838, 4474, 3253, 6891, 148, 3541, 9746, 6197, 6005, 10585, 7778, 3779, 4545, 583, 9530, 3565, 10273, 8186, 8666, 1406, 9482, 1861, 3037, ...], (1, 12): [5529, 10658, 126, 466, 9003, 2054, 841, 1407, 7227, 9963, 8739, 4451, 2390, 1814, 8451, 9723, 7851, 2150, 1838, 7947, 9363, 6605, 3947, 7995, 608, 6006, 5815, 5314, 3590, 1622, 8139, 2078, 1240, 6677, 1124, 2798, 5696, 9555, 4546, 2438, 512, 1598, 7635, 10490, 6246, 9195, 7035, 10754, 2294, 7563, 1079, 1503, 3422, 8979, 9387, 9939, 6198, 6438, 4163, 1031, 7779, 9483, 6988, 2702, 5672, 9747, 5648, 654, 937, 1862, 4475, 10298, 4331, 5481, 7467, 9339, 7203, 5935, 7827, 7251, 3804, 7323, 4930, 9291, 3374, 1055, 2630, 3038, 3494, 4498, 10586, 2222, 6486, 10730, 2942, 1101, 4714, 8907, 7491, 8571, ...], (1, 13): [10108, 3758, 2847, 9316, 2247, 6917, 585, 7276, 2703, 5530, 6247, 3948, 3734, 4523, 2607, 1839, 2079, 5075, 9940, 3039, 8764, 3495, 9220, 4883, 3111, 10875, 3231, 10012, 6989, 10155, 794, 2943, 10323, 7828, 8740, 2391, 1911, 8356, 10851, 4476, 313, 8188, 7084, 3159, 8212, 8932, 4140, 818, 3351, 4044, 2415, 3015, 1432, 3639, 4739, 3183, 150, 10659, 609, 8572, 6487, 3375, 9532, 1336, 6702, 6535, 5411, 2175, 1815, 7876, 5626, 6007, 6606, 7468, 2991, 3399, 842, 10683, 10491, 8908, 6511, 7780, 8020, 8140, 266, 9196, 9916, 4907, 443, 6678, 1125, 6055, 4715, 3543, 1647, 4356, 1032, 1623, 5673, 10731, ...], (1, 14): [4716, 3424, 6008, 8357, 6224, 245, 9005, 7829, 10756, 1219, 10085, 10276, 59, 1864, 7061, 2608, 7949, 7637, 3328, 8789, 6344, 2440, 2944, 4836, 5627, 2848, 5244, 491, 8573, 4572, 8837, 3112, 4908, 421, 7085, 5889, 1057, 10300, 7877, 1242, 1888, 2584, 267, 9581, 10324, 2200, 6176, 4117, 2056, 3184, 105, 3901, 9317, 3759, 7325, 9773, 3400, 1266, 1912, 3376, 2392, 223, 10684, 9965, 4980, 3520, 8645, 4548, 4668, 314, 10013, 6440, 3256, 6870, 1768, 6536, 9917, 2248, 6607, 6464, 4045, 9293, 7565, 2032, 6822, 468, 7397, 5340, 891, 7805, 3040, 9197, 10732, 1600, 749, 4333, 10348, 8909, 2560, 5292, ...], (1, 15): [6249, 3617, 6823, 1410, 8430, 8286, 1817, 4549, 7302, 3161, 6919, 2993, 8838, 7398, 8646, 773, 9990, 8574, 7206, 3545, 8550, 5269, 1434, 2153, 2393, 9006, 422, 1482, 6632, 2945, 9366, 2009, 7086, 4046, 1267, 4334, 5890, 5699, 3569, 2033, 5914, 9606, 515, 6225, 1865, 4741, 9918, 9774, 1985, 1913, 2225, 7662, 2585, 3353, 4573, 3521, 9150, 8190, 7278, 3807, 5842, 7494, 129, 940, 8214, 10757, 3926, 8766, 3783, 292, 6057, 9558, 8958, 10589, 964, 10493, 6105, 8742, 9462, 3113, 10014, 469, 2849, 587, 2369, 10134, 7015, 7062, 2561, 2081, 3377, 3497, 3736, 6273, 9966, 246, 6177, 6033, 5173, 1082, ...], (1, 16): [7879, 9343, 9559, 10087, 8263, 6681, 4359, 8407, 3234, 4838, 10686, 7063, 7999, 5557, 7231, 9391, 1221, 941, 917, 2826, 2538, 9967, 5819, 9919, 10566, 612, 9799, 9367, 5485, 4119, 8551, 6872, 8023, 8575, 9463, 8983, 2466, 7327, 797, 2082, 1083, 9751, 1650, 2202, 3903, 774, 10326, 751, 8647, 5102, 6082, 9007, 4598, 7831, 3642, 3546, 3042, 6226, 658, 1059, 10135, 7711, 10302, 3162, 2850, 8047, 10182, 1507, 1339, 4718, 7087, 4526, 2178, 1602, 7807, 7279, 6466, 2298, 1578, 8383, 3522, 6370, 6657, 5891, 7855, 8743, 5246, 8359, 6538, 247, 3354, 1035, 8791, 4862, 516, 1914, 1411, 9871, 1459, 2034, ...], (1, 17): [4048, 10088, 494, 4863, 2803, 5630, 2611, 10351, 3043, 1269, 3595, 8264, 5175, 1222, 1129, 85, 5127, 10303, 2203, 5558, 1340, 2947, 6443, 6107, 6778, 5916, 10759, 5725, 5103, 1508, 10543, 4743, 7280, 3163, 4336, 3785, 4456, 6179, 5007, 5510, 3115, 613, 5486, 3619, 659, 589, 424, 270, 5868, 3331, 1199, 4216, 2467, 6825, 8216, 7424, 10567, 6610, 5343, 10495, 2035, 7568, 10423, 8576, 5295, 6011, 4384, 3499, 1084, 6251, 7976, 6515, 2971, 3952, 2299, 2395, 5271, 154, 6921, 4144, 2539, 2443, 3643, 5247, 294, 9560, 822, 6539, 6993, 5319, 1747, 10159, 3355, 3809, 3571, 6083, 1867, 10711, 7832, 4168, ...], (1, 18): [3236, 2300, 4528, 3739, 2828, 4457, 5080, 1580, 4696, 2852, 2612, 1988, 5869, 4672, 10184, 3404, 8049, 660, 5726, 9969, 249, 1628, 425, 1820, 2084, 8193, 3810, 5320, 2564, 7713, 295, 2180, 10568, 3644, 682, 799, 2204, 5152, 1061, 5535, 6276, 4984, 1844, 3905, 10664, 4720, 3620, 2228, 6707, 1437, 2588, 5821, 1413, 3548, 2252, 3140, 63, 7569, 6204, 4744, 10856, 2804, 4912, 495, 4576, 86, 10113, 3596, 6060, 7881, 4504, 3977, 5487, 6108, 10352, 4361, 967, 1868, 895, 10280, 6468, 10760, 1200, 6994, 3356, 7089, 6180, 4481, 4217, 8577, 1748, 9009, 4193, 9801, 6826, 2036, 2972, 2948, 2012, 2876, ...], (1, 19): [6013, 8602, 7666, 2589, 5249, 5488, 9898, 10737, 2301, 426, 8626, 10138, 1224, 10833, 1629, 3787, 4913, 3357, 4745, 10329, 3021, 1342, 110, 10425, 4553, 2805, 4721, 2565, 2085, 2469, 10593, 7618, 1131, 6229, 4482, 10185, 9202, 10114, 1038, 661, 2709, 87, 10305, 5536, 3525, 8218, 683, 7019, 591, 3549, 2061, 6061, 9874, 7834, 3930, 7474, 5345, 5105, 3811, 8938, 519, 3573, 6277, 7498, 5894, 64, 7858, 3141, 3764, 1749, 9226, 9394, 5297, 6037, 8194, 2445, 3597, 8002, 8578, 319, 7090, 8434, 7330, 5918, 5321, 2421, 10569, 754, 6923, 2181, 8962, 7786, 7402, 4194, 1271, 4362, 5417, 8818, 1247, 9370, ...], (1, 20): [4483, 8555, 7307, 1248, 2254, 6852, 4722, 3812, 4506, 6038, 9731, 1774, 2398, 7043, 10139, 10594, 1870, 3598, 9563, 8531, 3741, 5704, 1202, 6014, 9755, 5489, 1846, 7979, 5298, 7235, 2950, 2038, 2086, 5322, 2638, 5154, 7811, 1654, 1343, 7595, 9803, 7835, 3166, 3907, 6182, 825, 2230, 755, 10498, 9611, 6518, 229, 2302, 4195, 7020, 1894, 3334, 8795, 5680, 520, 3118, 2422, 8219, 2470, 9779, 921, 9371, 9971, 88, 3502, 6685, 3622, 8003, 7667, 157, 10546, 1606, 2158, 6470, 3526, 7187, 4554, 6876, 9899, 5895, 4171, 8747, 8459, 4890, 10690, 1295, 8195, 9947, 9923, 111, 8771, 639, 10186, 2542, 9011, ...], (1, 21): [7428, 1655, 2951, 230, 3383, 593, 8292, 3575, 6375, 10667, 4675, 1847, 4603, 7716, 756, 10739, 9180, 8436, 2807, 6015, 5920, 66, 4555, 7596, 4340, 3647, 3766, 2159, 7788, 1631, 5538, 10571, 451, 3359, 2567, 475, 8796, 8364, 10835, 3335, 3527, 2639, 10116, 9324, 10883, 6853, 4915, 9588, 9396, 7236, 10140, 10331, 5107, 2855, 7620, 1440, 6877, 7308, 1296, 2039, 6710, 9012, 4939, 3551, 2879, 3980, 7836, 7021, 9468, 10427, 6997, 7668, 405, 7860, 6279, 1607, 5824, 4507, 1273, 6686, 9996, 7188, 4316, 5155, 2591, 4052, 9780, 9300, 10163, 6111, 1823, 2303, 6351, 1064, 3742, 640, 2231, 3932, 6231, 9732, ...], (1, 22): [4988, 1632, 8797, 7261, 9637, 3957, 6687, 1872, 5730, 8461, 7957, 7981, 9901, 522, 3408, 7477, 5180, 3790, 2016, 2568, 8005, 899, 3240, 6854, 6878, 6184, 2280, 641, 7717, 6376, 113, 2712, 971, 9877, 7813, 8389, 159, 8677, 10332, 3504, 5945, 803, 10572, 6088, 10884, 10308, 2208, 6926, 757, 3600, 10093, 4149, 7789, 2616, 2448, 8029, 10284, 9373, 2592, 8917, 8269, 1489, 6016, 9013, 2976, 3885, 3576, 5132, 2232, 1204, 7069, 7285, 6280, 8845, 1752, 7837, 4724, 10740, 5491, 2304, 4341, 9325, 7189, 299, 5420, 406, 8437, 9349, 9301, 3981, 4676, 9613, 6064, 1274, 9973, 2256, 6352, 90, 9517, 3933, ...], (1, 23): [1066, 3982, 3886, 6521, 3934, 9902, 4869, 10165, 2377, 9518, 5349, 9926, 10693, 523, 6449, 8366, 10885, 8942, 8582, 7478, 7406, 7214, 6185, 9590, 687, 8054, 10573, 8798, 7982, 9806, 2257, 1490, 4581, 3529, 8678, 8966, 4557, 3815, 3121, 10285, 900, 3241, 2881, 758, 619, 10429, 1514, 10765, 4174, 642, 10189, 7502, 3265, 3910, 4701, 137, 2641, 5946, 9734, 10741, 6879, 3553, 3337, 4917, 3169, 6712, 7790, 8126, 5085, 5564, 5874, 160, 2281, 3601, 9230, 4486, 948, 2041, 2857, 9566, 3625, 3505, 7958, 8462, 6041, 6089, 7094, 9950, 7070, 4126, 7718, 7046, 7886, 1753, 6377, 254, 1466, 8822, 4533, 114, ...]}\n"
     ]
    }
   ],
   "source": [
    "#考虑 是否是 工作日 和 小时 双因素，对用车数量的影响\n",
    "g=all_df.groupby(['workingday','hour'],sort=True)\n",
    "print(g.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a20bb26b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s5是双列 行索引\n",
      "workingday  hour\n",
      "0           0        94.489655\n",
      "            1        71.910345\n",
      "            2        53.748252\n",
      "            3        25.534722\n",
      "            4         8.544828\n",
      "            5         9.373239\n",
      "            6        19.993103\n",
      "            7        47.268966\n",
      "            8       112.255172\n",
      "            9       177.924138\n",
      "            10      263.806897\n",
      "            11      325.386207\n",
      "            12      373.923077\n",
      "            13      387.820690\n",
      "            14      378.731034\n",
      "            15      373.703448\n",
      "            16      364.763889\n",
      "            17      339.124138\n",
      "            18      292.248276\n",
      "            19      242.344828\n",
      "            20      183.806897\n",
      "            21      148.737931\n",
      "            22      123.351724\n",
      "            23       90.606897\n",
      "1           0        36.732258\n",
      "            1        16.003236\n",
      "            2         8.436066\n",
      "            3         4.892734\n",
      "            4         5.363636\n",
      "            5        24.529032\n",
      "            6       102.577419\n",
      "            7       290.690323\n",
      "            8       459.451724\n",
      "            9       242.293548\n",
      "            10      133.596774\n",
      "            11      157.019355\n",
      "            12      199.347267\n",
      "            13      197.160772\n",
      "            14      180.366559\n",
      "            15      198.627010\n",
      "            16      292.466238\n",
      "            17      446.131148\n",
      "            18      424.290196\n",
      "            19      348.012903\n",
      "            20      249.363344\n",
      "            21      184.855305\n",
      "            22      138.344051\n",
      "            23       88.996785\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#组内求均值\n",
    "import pandas as pd\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "df5 = g.apply(mean_numeric_columns)  # 对每个分组应用函数计算均值\n",
    "s5=df5['count']\n",
    "print(\"s5是双列 行索引\")\n",
    "print(s5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7a7d6358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "重置索引后的数据帧df6\n",
      "    workingday  hour       count\n",
      "0            0     0   94.489655\n",
      "1            0     1   71.910345\n",
      "2            0     2   53.748252\n",
      "3            0     3   25.534722\n",
      "4            0     4    8.544828\n",
      "5            0     5    9.373239\n",
      "6            0     6   19.993103\n",
      "7            0     7   47.268966\n",
      "8            0     8  112.255172\n",
      "9            0     9  177.924138\n",
      "10           0    10  263.806897\n",
      "11           0    11  325.386207\n",
      "12           0    12  373.923077\n",
      "13           0    13  387.820690\n",
      "14           0    14  378.731034\n",
      "15           0    15  373.703448\n",
      "16           0    16  364.763889\n",
      "17           0    17  339.124138\n",
      "18           0    18  292.248276\n",
      "19           0    19  242.344828\n",
      "20           0    20  183.806897\n",
      "21           0    21  148.737931\n",
      "22           0    22  123.351724\n",
      "23           0    23   90.606897\n",
      "24           1     0   36.732258\n",
      "25           1     1   16.003236\n",
      "26           1     2    8.436066\n",
      "27           1     3    4.892734\n",
      "28           1     4    5.363636\n",
      "29           1     5   24.529032\n",
      "30           1     6  102.577419\n",
      "31           1     7  290.690323\n",
      "32           1     8  459.451724\n",
      "33           1     9  242.293548\n",
      "34           1    10  133.596774\n",
      "35           1    11  157.019355\n",
      "36           1    12  199.347267\n",
      "37           1    13  197.160772\n",
      "38           1    14  180.366559\n",
      "39           1    15  198.627010\n",
      "40           1    16  292.466238\n",
      "41           1    17  446.131148\n",
      "42           1    18  424.290196\n",
      "43           1    19  348.012903\n",
      "44           1    20  249.363344\n",
      "45           1    21  184.855305\n",
      "46           1    22  138.344051\n",
      "47           1    23   88.996785\n"
     ]
    }
   ],
   "source": [
    "#重置索引 使得 原来的双索引，变成 属性 列\n",
    "df6=s5.reset_index()\n",
    "print(\"重置索引后的数据帧df6\")\n",
    "print(df6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "179b50a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AS+lTFY3sEFtWpJJMMKibce3Vlpu3vgUNi9Nv9GS4nvV1sXDhQvzvf//D2rVrkZOTgxdeeAF5eXmYM2eOye9lKpRnhxBboTyy4xUMuPvxH/eNBKruzc9X3ASaGgAnF8v1jxBTMWTbuSayXYvmqm9BieG+6B4m0mmRckKYCD3CfU3eh8mTJ+Pu3bt47733UFhYiPj4ePz999+IjIzU/mSB0MgOIbagplQRyAD8UR0Z3rodlksuSIgtUj0mQtdt5+qo5t3RJmaMcfczI4Zh8OnEBPi4aR6r8HFzwicTE8y2aHjevHm4fv066uvrkZWVhYEDB5rlPqZCwQ4htkBd5mRltCOL2INmx0SENR/F1FdYsu7Zk0N7coeMWrHoIG/8NrcfuoeJWnw8IUyE3+b2M1sGZVtE01iE2IJm285bGNlRXaRM63aILWp2TISRU1gAd2jo+FQuj06dhukfNxEwfiVX38pFB3njz/n9kZ1fjt3niyGubYTI3RnD44LQI9zXqreBC4GCHUJsQUunnatS3X5OIzvEFpl6CksmMBaYuQvYMpdLINiSBxa3PGpqpRiGQWKEHxIjjBz5agVoGosQW6DTyA7l2iF2QDXYMcXIjkxgLDB7PzBrLzDgBSBqEP/xPOs97oAYh0Z2CLEFJUrBjmcg4OHfvI4ojDsLSJafhEZ2iC0y9bZzVQzDreEJSwYaaoBPo4GGewdm5mwD6ioANx/T3pMIjkZ2CLF2tWVAlVK2UuXMycocnbnFnDI0skNskfK2cyc3wL+D+e7l4gHEjVOUm2qBnL/Mdz8iGAp2CLF2mjInq1Jet1NzB6ivMk+fCDEHUx4ToauEx/jlUxvNez8iCAp2CLF2mg4AVaW6body7RBbUpKjckyEiRYnaxLZHxCFK8rXDwLleea/L7EoCnYIsXaGjuwANJVFbEuzzMkWCHYcHIDuk/nXTv9i/vsSi6JghxBrV6J6JpYeIzu0SJnYEnNtO9em2VTWL1xyQ2I3KNghxNopj+x4tgU826ivSyM7xJaZ+kwsXQV05mdNvnsZuHnCMvc2BssC+ceBPYuBrQu4P/OPmzVQ++eff/DQQw8hNDQUDMNgy5YtZruXKVlNsLN06VIwDIMFCxbIr7Esi8WLFyM0NBTu7u4YPHgwzp3jvxjq6+vx7LPPIiAgAJ6enhg7diwKCgos3HtCzKS2HKi8pShrGtUBaGSH2C6WBYrOKMqmOCZCHwmP88unfrbcvQ1RkgOsHgKsSQEOfQ5kreP+XJPCXVcdETaR6upqJCQkYMWKFWZp31ysItg5fvw4Vq1ahe7du/Ouf/zxx1i2bBlWrFiB48ePIzg4GMOGDUNlZaW8zoIFC7B582Zs3LgRhw4dQlVVFcaMGQOJRGLpb4MQ07tziV/WFux4BQGOrooyjewQW1FxC6grV5QtNYUl03UC4OCsKJ/9DWhqsGwfdFWSwx19oS4T9K2T3ONmCHhGjRqF999/HxMmTDB52+YkeLBTVVWFJ554AqtXr4afnyKKZ1kWy5cvxxtvvIEJEyYgPj4e3333HWpqavDTTz8BAMRiMdasWYPPPvsMKSkpSExMxIYNG3DmzBns2bNHqG+JENPRJXOyMgcH/hlZ5Tdo7QGxDebMnKwLzzZA9AhFubYMuLzbsn3QBctyR15oOuML4B7fMo9e//cIHuzMnz8fo0ePRkpKCu96bm4uioqKMHz4cPk1V1dXDBo0CBkZXErvrKwsNDY28uqEhoYiPj5eXqcl9fX1qKio4H0RYpVK9Nh2LqO8bqe+gn+oIiHWSnkKC7DMTixVzRYqW+FUVkGm+hEdVbdOADezzNsfGyFosLNx40acOHECS5cubfZYURGXMTYoKIh3PSgoSP5YUVERXFxceCNCqnVasnTpUohEIvlXeHi42rqECEp1ZEeXQwpp3Q6xRUJsO1fVeTh/ndClXUBNqeX7ocnF7frVv7DNPP2wMYIFO/n5+Xj++eexYcMGuLm5qa2nekw9y7Jaj67XVue1116DWCyWf+XnU+I1YqWUgx2PNoBngPbn0I4sYouUp7Gc3IA2HS3fBydXbu2OjLQROPeH5fuhSW25eevbKcGCnaysLJSUlCApKQlOTk5wcnJCeno6vvzySzg5OclHdFRHaEpKSuSPBQcHo6GhAWVlZWrrtMTV1RU+Pj68L0KsTl0FUHFTUW6rw6gOQCM7xPY01gJ3ryjKgbGAg6MwfWm2K8vKjo9w9zVvfTslWLDzwAMP4MyZM8jOzpZ/JScn44knnkB2djY6dOiA4OBgpKWlyZ/T0NCA9PR09OvXDwCQlJQEZ2dnXp3CwkKcPXtWXocQm9VsJ5aWxckyNLIjOJZlcSKvDB/tvIDXN5/BRzsv4EReGVhaLNoyIY6JUCcsGfBXGlUqOA7cuaK+vqV1Ga1f/ZgxJr19VVWV/D0b4NbXZmdnIy/Puo/YMPMJa+p5e3sjPp7/A+3p6Yk2bdrIry9YsABLlixB586d0blzZyxZsgQeHh6YMmUKAEAkEuGpp57Ciy++iDZt2sDf3x8vvfQSunXr1mzBMyE2R3XbqC7rdQAa2RHYpeJKvLTpFE4X8HfLpB64iu5hInw6MQHRQd4C9c5Kqa7XCe4mTD8AgGG40Z397yuund4IDH1TuD4pC0sGQhN1W6Qc2pOfLNEEMjMzMWTIEHl54cKFAIDp06dj/fr1Jr2XKQkW7Oji5ZdfRm1tLebNm4eysjL06dMHu3fvhre34hfF559/DicnJ0yaNAm1tbV44IEHsH79ejg6CjQESoip6LvtXMbdD3D14XZiATSyY0GXiivxaGoGKuqaWnz8dIEYj6Zm4Le5/SjgUSb0tnNV3Sfxg51TvwCDX+dSOwiNYYDxqVweHU3bz91EwPiVXH0TGjx4sE2OUFrB/5zCgQMHsHz5cnmZYRgsXrwYhYWFqKurQ3p6erPRIDc3N3z11Ve4e/cuampqsHXrVtpdReyDPqedK2MY/uhOeR4glaqvT0yCZVm8tOmU2kBHpqKuCYs2nbLJNwyzKbKyYMcvEogcoCiL84C8f4Xrj6rAWGDmLm6EpyWhPbnHdR0NbgWsemSHkFZN+Uwsd3/uXCxd+UUCxffylkjqgapiwCfEtP0jPCfzy5tNXalzqkCM7PxyJEZY8DgEa8Wy/JEdSx8ToU7CZODGIUX51M9A+/7C9UdVYCwwez+XR+fCNm7Xlbsvt0anXZLJR3RsHQU7hFij+kpArJQSoW2Mfr+8Wlq3Q8GOWaWdL9ar/o6zRRTsANyOQyGPiVAnbhzw9yKgqY4rn9sCPPgJ4OwuaLd4GIZbwxOWLHRPrJ5VTWMRQu65rbITK1DHKSwZ2pFlUXeq6nHgYolez1l3OBcv/JKNXeeKUNfYis/ys4Zkgi1xEwExSjufGiqBC3om9CNWg0Z2CLFGhq7XkVE+HwugHVlmUlJZh1Xp17Dh6A3UNeq3LqpRwmLzyZvYfPImPFwcMSQmEKPigzGkSyA8XZv/amZZFifzy5F2vhji2kaI3J0xLC4IieG+WhOtWrVmx0QIvF5HWcLjwNnfFeVTG4Fuj8qLtO7KMkzx70zBDiHW6LbKtnNdd2LJqE5j0ciOSZVU1uHb9Gv40YAgpyU1DRJsP12I7acL4erkgEHRbTGqWzAeiA2Cj5uzfW9nV92JJeS2c1UdhgCegUD1vVG7q3uBymI4e3CZzGtqauDubkXTWnaqpqYGAODs7KylpnoU7BBijZQXJwO6Z0+WoZEdsyipqMM394Kc+ibDgxyRuzMamiSobSFQqm+SYvf5Yuw+XwxnRwY9wn1x9mYFatVMddn8dnblaSwnd8C/g3B9UeXoxG1D/3cFV2alwJlNcOz3DHx9fVFSwgVBHh4etj26ZqVYlkVNTQ1KSkrg6+trVEoZCnYIsUbKp527+QJegfo939UL8AgAau5wZRrZUUuX6aHiijqkHriKn4/ltRjkMAzwUPdQPNQ9BC9q2X7u4+aETXP6IsLfA+mXbmPn2SLsOV+Myvrmz2mUsDh+Xfup9bLt7Fvm99f5TdcqpsWs6ZgIdbpPVgQ7AJdgsN8zCA4OBgB5wEPMx9fXV/7vbSgKdgixNvVVXF4PmcBYw7aR+kUqgp2KAkDSCDgaPgxsj7RND706Mga7zxfjp2N5aGghyHFggLEJoXhmaGd0CvQCAPwW4NlimwCQECbCJ0pTTiO6BmNE12DUN0mQceUudpwtRNr5YpTVNOr9vZwqEOPg5TsYGK09RYHVTIs1OybCitbryAR3AwK7AiX3RqCKzgBFZ8EExyMkJASBgYFobNT//4voxtnZ2SRJginYIcTaGHomlirfSC4HB8C9oYgLAP8o4/pmR3TJdjzlf0dbfMyBAcb1aIdnhnZCx7ZevMeig7zx5/z+yM4vx26lUZPhcUHooWbUxNWJW6A8JCYQTRIpjuaWYsfZQvyedVPt9FVLpq09hmAfN8SEeCMm2Aex9/7s0NYTzo4OOn/fFpsWs+b1OjIMAyQ8BqS9pbh2eiMQzGVYdnR0pIz9NoCCHUKsTbOdWAZmQVXdfl5+g4Kde3TNdqzKgQHGJ7bDM0M6oYNKkKOMYRgkRvgZlEfHydEB/TsFoH+nAEikLH4+lq/9SUqKKupQVFGHAxdvy685OzLoFOiNmCAvHMkt1TnLsz7TYgax1m3nqrpNBPa8oxiFOv0r8MBibk0PsQn0P0WItTH0TCxVtCNLLX2yHQNckPNwYhieGdoJUQGeZuwZn6+Hi0naaZSwyCmsQE5hhc7PsUiW52bHRMSZ717G8AnhdmZd3cuVq4qB3ANAJzpw2lZQUkFCrE2JkTl2ZJqN7OS1XK8V0jfb8WO9IvDZpASLBjoAMCwuSK/63dr5wM/DdOuyduv576QXllUcaQIAonDrOCZCnYTH+eVTG4XpBzEIjewQYm2UR3bcRIC3gbsQWjoyggAAxLV6LigVaFdxYrgvuoeJdBqFSggTYct87uym25X1yCmqxIXCClwoqkROYQWu3q5Co0S/5GzbTxciNsQHQ7q0hbebiRe3V9zkn9ptjYuTlcWMBly8gIYqrpyzjTvWxdUGt/u3QhTsEGJNGqr5IzD6nomlTBQO7l363hscTWPJidz1e+PWt76pMAyDTycmaFxQDHDb2T+ZmCBfXxPo44ZAHzcMUtqZ1dAkxZtbzuDXzAKd759XWoPnfj4JF0cHDOgcgJFdg5ESFwR/z5an1/Tazt5sCstK1+vIuHhw52Vl/8iVm2qB838BiU8I2y+iEwp2CLEmdy5BHpwAhk9hAYCTC+DTjtt2DtDIjpJhcUFIPXBV5/rD9ZxOMqXoIG/8NrefztvZ1XFxcsBjvSP0CnZkGiRS7LtQgn0XSuDwB9Anqg1GxgdjeNcghIi4DMJ6b2dvthPLyoMdgNuVJQt2AO4kdAp2bAIFO4RYk2aZk40IdgBu3Y4s2Kkq5pK4WdOpzQJJDPdFt3YinLmp2/RQj3Bf83dKA0O2s7dEn2kxBwaQtjDrJWWBf6/dxb/X7uKdv84hIdwXSRF++DUzH1UtJEYE1GxnVw12rH1kBwAiBwA+YYrX1PWD3EisasZyYnVogTIh1qTEyDOxVDVbt0OLlAFueqhfpzZa66lODwlJtp39lZExWPJwN7wyMgaJEX569U02Lebjpvlzro+bE7Y+MwDfzeyNx3tHIMBL/a6wU/nlWHs4V22gIyPbzi4/1NGaj4lQx8EBSJjMv3b6V2H6QvRCwQ4h1kR1ZCfQwBw7Mqo7smjdDgDgblU9fjqqOfBLCBPZ7nlTGsimxbqHiVp8XPZ9d20nwqDotlg6oRuOvp6CTXP64qkBUWjna/jIoGw7u00cE6FO98f45VMbuZ1lxKrRNBYh1kR5J5arD+AdYlx7tCOrRcvSLqFSacFvt3Y+GNApAOK6JoOmh2yNvtNijg4MerX3R6/2/nhzdCzO3arAzrNF2HmuCFdKqvS69+7zxUh0rOYfE2EL63Vk2kYDoT2BWye48t3L3N/bJQnbL6IRBTuEWIuGGqDsuqJszE4smWYjO9dbrNaa5BRW4OdjilEdBwb4ZGICYoJ9BOyV5Rma5ZlhGMS3EyG+nQgvjeiCZ346gW2nC3V+vri20XYyJ6uT8Lgi2AG40R0KdqwaTWMRYi3uXgZ/J5aR63UAGtlRwbIs3tt6nrfwdkqfiFYX6JhSuL+HXvVF7s62t+1cVfwjgIPSWMGZ34CmBuH6Q7SiYIcQa2GqzMnKvEMAR6XFpa18zc6uc0X499pdednHzQkLh5kgqGzF9M3yPCw2qIWRHStPKKjKsw3QeYSiXFsKXEkTrj9EKwp2CLEWqmdiBZog2HFwuJdc8J5WPLJT1yjBB3/zd7stSIlWmyCP6Ea2nV1Xf2Tlg212TISv6TtmbgmqC5V/FqYfRCcU7BBiLUydY0dGed1OnRioLTdNuzZmzaFc5JfWyssd23piat9IDc8gutB1O7vMvmMnwfCOibCxKSyZ6BGAm6+ifHEnUFMqWHeIZhTsEGItbiuNOrh4c9mPTYHW7aC4og5f77/Cu/bWmDg4O9KvQFPQtp3dy1WxrTzGQWXLv61NYck4uQLxExRlaSNwbrNw/SEa0W4sQqxBY63KTqwuxu/Ekmkp105IgmnathEf77yImgaJvDw0JhCDuwQK2CP7o2k7e2yINxb+egp/nylCLMMPdir9YmCzmYwSHgcy1yrK/3wCFJ3hpuW6jAbCkk33OiZGoWCHEGtw5zI/74gp1uvItPKRnVP55fj9hOI8KCcHBm+MNjJZI2mRpu3sXz3eE74eZxF7gh/sPLO3Hks71CLUiGSFggnrxT8+orIQyFrH/f3Q50BoIjA+1fjkoMRoNIZLiDUw13odoHmw04p2ZLEsi3e38nf+TO/XHh3begnUo9bL0YHBB+Pj0c9LkZOnlnXBwbsiPJqaoXdyQqtw+wJQc0f947dOAmtHND8GhlgcBTuEWIPbqmdimTDYUZ3GakUjO3+duoUTeeXysr+nC557oLNwHWrlmMZa+Nfly8sX2XBI4YBb4jpM/CaDO0rCVrAssGUu0FSnuV6dGNgyj46UEBgFO4RYA3OO7Hi0AZw9FeVWMrJT09CED3fwt/O/ODyaS2pHhHE7hzdde5FVnBZeVtOIKauP4ODl20L0TH8FmdzIjS5unQBuZpm3P0QjCnYIsQbKOXZcvABRmOnaZhj+6E55Xqv4lPlN+jUUihWfumOCvfFYrwgNzyBmp5I5ObH3/XBzVrwN1TRIMHP9cWw7fcvSPdPfxe361b+wzTz9IDqhYIcQoTXWAaXXFGVT7sSSUV6301QLVJWYtn0rc7O8Ft+mX+Vde+ehrnB0oJ0xglLJnBzd7T78OOs+3mhbo4TFsz+fxA//Xrdw5/Skb76qVprfylrQbixChHb3Cn8nlimnsGRaWrfjrV+af1uy9O8c1Dcp/k1HxQejb8c2AvaIAACKVc/E6ookd19smtMX09YcQ1EFNxLHssBbf57D3eoGPDe0E7ILxEhT2s4+LC4IiUKfSq9v1mdbzBJtRyjYIURoqsdEmOIAUFUt7cgK7236+1iBY7mlvFO4XZwc8PqDtPVXcCzLD3ZEEfIAgEtKyAU81+5Uy6ss33MZP/x7A3er+Ydsph64iu5hInw6MQHRQQJl6ekymtterquYMebrC9GKprEIEVqzYMcMb8zNRnaum/4eVkAqZfHeNv5Uyez7o/Q+mZuYgbiA25kko5I5OczPA5vm9EW3dvwszKqBjszpAjEeTc3ApeJKk3dVJ2HJXB4dXYT2BNolmbc/RCMKdggRmlAjO3bot6wCnL1ZIS8Herti3uBOAvaIyKmedB7c/EysNl6u+Pm/96FvB3+dmqyoa8KiTafACrHgnmG4hIFuWg5BdRMB41dSJmWBUbBDiNBKlIIdZ0/+KeWm0mxkJ6/lejassq4RH+/iB46vjIyBpyvN1lsF5ZPOAbVnYnm5OmHBsGidmz1VIBYuP09gLDBzl/oRHr8o7nHKoCw4CnYIEVJTvcpOrGjAwQwvS1dvwF3p07IdJhZcsf8K7lQppjwSwn3xcKKJDlMlxitSXZzcTW3VAxf1y7Wz+3yxIT0yjcBYYPZ+YNZeIG48/7HI/hToWAkKdggR0t0rAKs4oNIsO7FklEd3xAWAVKK+ro25fqcaaw/l8q69PSYODrTV3HooT2M5uQP+UWqrimsb9Wpa3/omxzDcGp7xKwFHV8X1q/taRU4rW0DBDiFCarZex4zBjvK6HWkTUHHTfPeysA/+zkGjRPGmMr5HKJIimx9GSQTSUAOUKuU9CooDHBzVVtc3y7XVZMV28QQi+ynKlbeAkvPC9YfIUbBDiJDMeUyEKtV1O3aySPnQ5TtIU5rGcHd2xCujzPjvSPSnckwEgpovTlY2LE6/HFDD9axvVp2H8cuX04TpB+GhYIcQIamehhxooZEdwC7W7TRJpM22ms8d3BEhIneBekRa1Gy9juZgJzHcF93DtOxyuichTIQe4b4GdswMOqkEO1f2CNMPwkPBDiFCUh7ZcfbgEq2Zi52M7LAsixN5Zfho5wU8vvoILhVXyR9r5+uO/w7sIGDvSItUMye3sO1cGcMw+HRiAnzcNO+kc3VywCcTE4TNpKwqoDP/dZx3BKgXKBcQkaNghxChNDXw1zEEmGknloxve37ZBkd2LhVXYtzXhzFhZQZSD1zF8etlvMdn9IuEm7P6tSBEIKo5dgLjtD6Fy6rcT+MIj0TKwtnRyt7GGAbonKIoSxuB3H+E6w8BQMEOIcIpvcotFJYx53odAPANB6D0CdjGRnYuFVfi0dQMnC4Qq63z1b4rwmXUJS1jWf40ltIxEdpEB3njz/n9sXleP8wd3BFT+kSgY1tP+eNNUhbvbj0nTFJBTTql8Mu0bkdwFOwQIhTV9TrmyJyszMkV8A5RlG1oZIdlWby06RQq6po01hM0oy5pmbgAqFcKULVMYaliGAaJEX54ZWQMljzcDRtm9YGHi2L07sDF29iTU2Kq3ppG1EDAQWmH2JU9tAVdYBTsECIU1Z1Ylkg+prxup7IQaKwz/z1N4GR+ucYRHWWCZtQlzbVw0rkxQkTueHZoZ961d7eeQ12jFeWNcvUGIu5TlMX5wJ1LwvWHULBDiGAscSaWKtUdWeJ889/TBNL0zJAraEZdwtcs2NFvZKclTw2IQocAxXRWQVktvkm/quEZAqAt6FaFgh1ChKIc7Di5Nw9EzMFGd2TZXEZdoqDntnNduDg5YPFY/ghR6oGryC+tMbptk6Et6FaFgh1ChCBp5I6KkAnorDGjrMk0y7Vz3fz3NAGbzahL+CM7zh4aj4nQx8DothjZNVherm+S4t2tVpStODAW8A5VlG8cBhqqhetPK0fBDiFCuGvhnVgyNjqyY9MZdVuzhhruZ10mMNakQf2bY2Lh5qx4G9uTU4z9F6xksbLqFnRJA3D9kHD9aeUo2CFECKrrdcyZOVmZjWZRTgz3RWyIt051rS6jbmtWkgNAaReSCaawlIX5eWD+4E68a+9uPYf6JitZrExb0K0GBTuECMGSZ2Ip8wkFHJSy0trIyA7DMEgI89Vaz8fNyfoy6rZmzTIndzP5LWYP7IDINh7y8vW7NVj9zzWT38cgHQYDjNJIFq3bEQwFO4QI4bZqjh0LBTsOjoAoTFG2kZGdqvom/H2mUGOdhDARfpvbD9FBuo0AEQsw8bbzlrg5O2LxQ/x2V+y/goIyK1is7CYCwvsoymW5/Gk9YjGaDx4hhJiH8siOoyvg195y9/aNBMquc3+vLQPqKgA3H8vd3wC/Hs/nJRTs3d4fSe39IK5thMjdGcPjgtAj3JdGdKyN6jERZgh2AGBITCBSYgPlyQXrGqX4YHsOUp9MMsv99NI5BcjLUJQvpwFtOgrXn1aKgh1CLE3SCNy5rCgHRFtmJ5aMXySQq1Quv2GW6QVTaZJIseZQLu/ayyO7ILm9v0A9Ijpp6ZgIN91OMjfE22O64p/Ld9DQJAUA7DhbhIOXb+P+zm3Ndk+ddEoB9r6nKF/ZA9w3R7j+tFI0jUWIpZXmcocDylhqcbKM6iJlK1+3s+NsEW6W18rLiRG+SIr0E7BHRCfifKOOidBXRBsPzB3EHzF5569z8uBHMMHdAS+l3YHXDwKNterrE7OgYIcQSxMic7Iy1SkzK163w7IsVqksNv3v/R1ousoWNJvCMm+wAwBzB3dEmJ+7vHztdjXWHs7V8AwLYBj+rqymOi7nDrEoCnYIsbRmwY4FzsRSZkMjO0euleLMTcXoQGQbDwxXSiRHrFizzMnmWa+jzM3ZEW+PieNd+3LvZRSKBR5J6fQAv3yZdmVZGgU7hFhas2DHwtNYqokFrXhkZ/VB/qjOrAFRcHSgUR2bYIFt5y0ZFheEwV0U63RqGiT4YHuOhmdYQIchAKP0dktb0C2Ogh1CLK1EKdix9E4sAPBsy6Xtl7HSkZ3LxZXYp5QN18/DGY8mhQvYI6IX1WMiLPRzzjAM3nmoK1wcFW9v204XIuPqHYvcv0Ue/kC7ZEX57mXFjkhiERTsEGJJkibuF51MQGfA0cKbIhkG8I1QlMtvcDtnrIzqqM7U+yLh7mLBXWvEcA3VKsdExFl0x2FUgCdmD+SfwfXOn+fQKBFwsbLqKeg0umNRFOwQYkll17kzcmQsvThZRnndTmMNUHNXmH6oUVJRhy0nb8nLLk4OmNavvXAdIvopuQD+MRHmX6+jav6QTggVucnLl0uq8F3GdYv3Q47W7QhK0GAnNTUV3bt3h4+PD3x8fNC3b1/s2LFD/jjLsli8eDFCQ0Ph7u6OwYMH49w5/gr/+vp6PPvsswgICICnpyfGjh2LgoICS38rhOimWeZkCy9OlrHyA0G/+/c6GpQ+hT/SMwwBXq4C9ojopfgMvyxAHicPFye8qbJYefmeyyipqLN4XwAAIYmARxtFOfcfoKlemL60QoIGO2FhYfjwww+RmZmJzMxMDB06FOPGjZMHNB9//DGWLVuGFStW4Pjx4wgODsawYcNQWVkpb2PBggXYvHkzNm7ciEOHDqGqqgpjxoyBRGIlB8ERokzobecyzQ4EvS5IN1pSXd+EDUfyeNdm3R+lpjaxSgJsO2/JqPhgDOgUIC9X1Tdh6Y4LGp5hRg4OQEel0Z3GaiDvX2H60goJGuw89NBDePDBBxEdHY3o6Gh88MEH8PLywpEjR8CyLJYvX4433ngDEyZMQHx8PL777jvU1NTgp59+AgCIxWKsWbMGn332GVJSUpCYmIgNGzbgzJkz2LOHhgiJFSpRPe2cRnZU/ZqZD3GtIuliSmwQOrb1ErBHRG/Ntp3HtVzPzBiGweKxXeHsqNjBt/nkTRzLLRWkP83W7dAp6BZjNWt2JBIJNm7ciOrqavTt2xe5ubkoKirC8OHD5XVcXV0xaNAgZGRw54xkZWWhsbGRVyc0NBTx8fHyOi2pr69HRUUF74sQi1A+E8vBGfATaMSi2ciOdQQ7LR0N8fSgDgL1huiNZYH8Y8DNLMU1ryDAVbiz1zoFemHmAP7r7O0/z6JJiMXKHYcCUEqdcGWv5fvQSgke7Jw5cwZeXl5wdXXFnDlzsHnzZsTFxaGoqAgAEBQUxKsfFBQkf6yoqAguLi7w8/NTW6clS5cuhUgkkn+Fh9N2VmIBUglw55KiLMROLBkrHdnZea4IBWWKBHA9wn2RTEdD2IaSHGD1EGDNMECitBalqpi7XiJcrptnh3ZGkI9izdeFokrMWHcMr28+g492XsCJvDKwltiR6BkAhCYqyrdzADGtMbUEwYOdLl26IDs7G0eOHMHcuXMxffp0nD9/Xv64alp4lmW1porXVue1116DWCyWf+Xn5xv3TRCiDcsCOX/x3wQ8A4Xb8u0mAtx8FWUrGNlhWRarVY+GGEhHQ9iEkhxg7Qjg1smWH791kntcoIDHy9UJb4zmT6UdunIXPx3NQ+qBq5iwMgPjvj6MS8WValowIdqCLgjBgx0XFxd06tQJycnJWLp0KRISEvDFF18gOJhLCa86QlNSUiIf7QkODkZDQwPKysrU1mmJq6urfAeY7IsQs5F94t00g38994Cwn3iVR3fK87mRJwEdzS3FqQLF0RDh/u4YQUdDWD+WBbbMBerEmuvViYEt8wQL8LsEeWnMvn26QIxHUzPMH/Aon5MF0LodCxE82FHFsizq6+sRFRWF4OBgpKUpfhAaGhqQnp6Ofv36AQCSkpLg7OzMq1NYWIizZ8/K6xAiKGv+xKu8bkfaCFQWWr4PSlRHdWYN6EBHQ9iCgkz1P9+qbp3gr+exEJZlsei305BINQdaFXVNWLTplHmntNol8UdVr6UDkka11YlpCBrsvP766zh48CCuX7+OM2fO4I033sCBAwfwxBNPgGEYLFiwAEuWLMHmzZtx9uxZzJgxAx4eHpgyZQoAQCQS4amnnsKLL76IvXv34uTJk3jyySfRrVs3pKSkaLk7IWZm7Z94rWjdzpWSSuxVOhrC18MZE5PDBOsP0cPF7frVv7DNPP3Q4GR+OU4XaHkd3nOqQIzs/HLzdcbB8d5C5XsaKoH8o+a7HwEACLQ6klNcXIypU6eisLAQIpEI3bt3x86dOzFsGDen+fLLL6O2thbz5s1DWVkZ+vTpg927d8Pb21vexueffw4nJydMmjQJtbW1eOCBB7B+/Xo4OlJaeSIwQz7xhiVrr2sqLe7I6m+5+yv530H+Dqyp90XCw0XQX09EV7Xl5q1vAmnni/Wqv/t8MRIjzLgwvvMw4NwfivLlNKD9APPdjwgb7KxZs0bj4wzDYPHixVi8eLHaOm5ubvjqq6/w1Vdfmbh3hBjJkE+8lgx2VA9mFGhkp6SyDn+cuCkvuzg6YFrf9oL0hRjA3de89U1AOW+TOerrraPK0RFX9gLD3jXvPVs5q1uzQ4jdsPZPvFaSa+f7jBu8oyEm9GyHtt50NITN6DJav/oxY8zTDw1E7s5mra837yAguLuiXHwGqBB2zZy9o2CHEHOx9k+8vir5pQQY2alpaMIPR/j3nXU/JRG0KWHJ/NwxmoT25BboWtiwOPW7c1syXM/6BlHdgn6VEgyaEwU7hJiLtX/idXbnstvKCDCysymzQOVoiEB0CqSjIWwKwwDjUwFnT8313ETA+JVcfQtLDPdF9zCRTnUTwkToEe5r3g4BtAXdwijYIcRcbOATL28qq+KWRU9hbpJI8b9D/O3ms2lUxzYFxgLRI9Q/HtoTmLlLsLPgGIbBpxMT4OOmfZnq04MslMgyrDfgqhSAXdsPSJrMf99WioIdQsxF9onXTcsnSgE/8fK3n7MWTV2/61wx8ksVR0MkhInQO8rfYvcnJiQ7E0vOAejxJDDgBWDWXmD2PuEOvb0nOsgbv83tp3WEZ8ORPMscHeHoBHQYpCjXiYGbmea/bytFezsJMafAWO4T7bcDAUlD88dDe3KBjlBvBKqLlMuuA206mv22LMti1T9Xedf+O7AjHQ1hq4rPAhVKgXKHQcD4r4XrjxrRQd74c35/ZOeXY/f5YohrG+Hm5IBfMvNRXc9lEM+4ehf7LpTggVgLrdvJ+UtRvrIHiLjP/PdthSjYIcTc/DvwM6R6tAV6Psmt0WmXJMyIjoxqYkELrds5fr2shaMhLPDmQszj0k5+OXqkMP3QAcMwSIzw4+XRCfV1x/vbFVnMP/g7BwOj28LZ0cyTH6pb0C+nAUPfNO89WymaxiLE3MquA1AaFu/0AJCymFvTI/RIRrORHcsEO6tUjoZ4qn8UnMz9xkLM56JqsDNcmH4YaFrf9ohs4yEvX7tdjZ+P5Zn/xqJ2QGBXRbkwG6gqUVudGI5+uxBibnev8MsBnYTpR0sEGNm5UlKFPTmKjLYid2dMTA7X8Axi1apK+OddBXThRjNtiIuTA14bFcO79nnaJfMnFwS4Dz/Kru4z/z1bIQp2CDE31WCnjRUFOz5hAKN0tIoFRnbWqOzAmnpfJDxdaUbdZl1OA2/ksov1TmFpMqJrMHq3VyyQL6tpxMr9VzQ8w0RU8+3QFnSzoGCHEHOz5mDH0YkbSpcx88jO7cp6/K56NES/SA3PIFbPhtbraMIwDN4Yzd8osO7wdeSX1pj3xuH3AS5KuaWu7gOkEvPesxWiYIcQc7vL33VkdUP8yut2au4C9VVmu9UP/15HQ5PiaIiHE9sh0NvNbPcjZtZUz592cfPl8sfYqIRwX4zvESovN0ik+HDnBfPe1MkFiFLagl5bqvsBwkRnFOwQYm7KIzs+7QAXLZlmLc1C63ZqGprwfbOjIaLMci9iITcOAw1KwXHnYdxooQ1bNDIGrk6Kt8btpwuRdaPMvDftrJJN+coe896vFaJghxBzqqsAqhSLcS2Rw0Zvvu355XLz7EL5LasA5TWKBZ9DYwLROcjbLPciFnJpF79so1NYytr5ujcLwt/fft68iQbp6Aizo2CHEHMqVZnCsqb1OjKqIzsmXKTMsixO5JXhwx05+HjnRd5j/x1oZdN5RD8sC1zcoSgzjs13FtmouYM7IcDLRV4+mVeObafNeCq5bwS3i03mZhZQU2q++7VCFOwQYk6q63WsMdhRzbVjommsS8WVGPf1YUxYmYFv0q+hql5x7o+7syP8PJxNch8ikNsX+T8rEX0Bdz/19W2Il6sTFg7rwrv24Y4LqGs048Jh3ugOS1vQTYyCHULMyZp3YsmYYWTnUnElHk3NwGmlLMnKahslmPjNv7hUXGn0vYhAVHdh2eiWc3UmJYchOkixS+pmeS3WZ1w33w1V1+3QVJZJUbBDiDnZQrDjFQQ4Ke2IMnJkh2VZvLTpFCrqNJ/gXFHXhEWbTlnm0EViena4XkeZk6MD3hgdx7v29b4ruFtVb54bRvQDnBVZnHF1LyCVqq9P9ELBDiHmpBzsODhxc/PWhmH4/Sq7wa3HMNDJ/HK1IzqqThWIkZ1fbvC9iEBqSoH8I4qyfwfrDOSNNCi6LQZGt5WXK+ubsHzPZfPczNkNaH+/olx9Gyg6ZZ57tUIU7BBiLizLX7Pj1x5wtNJ1KsrrdhoqgVrDt9qmnS/WXknJbj3rEytwZQ/AKo06RI8U/pw3M3njwVg4KH1rPx3Lw5USM02/qmZTpi3oJkPBDiHmUn0bqK9QlK35k2+zdTvXDW5K3/OELHL+EDEtO8marIsuwd6Y3Esx8imRsljyt5kSDaruZrtMwY6pULBDiLnYwnodGRPuyBK56zd6pW99IjBJI3/EwdWH24llxxYOi4ani+IMuX0XSnDo8h3T38i/A+CvlIur4JhRo6xEgYIdQsylWbBjhQkFZUy4I2tYXJBe9YfrWZ8ILO8IUKe0JqvjUO7IAzvW1tsV84bwP6y8v/08JFIzLK5X3oLOSoFrB0x/j1aIgh1CzKWVjuwkhvuie5hIp7oJYSL0CPc1+F5EAM22nI8Sph8W9tSAKISKFLsWLxRV4vesAtPfiNbtmAUFO4SYiy0kFJQx4cgOwzD4dGICnBw0L1j1cXPCJxMTwNjpwla7xdtyzgCdhqmtak/cnB3x8sgY3rVPdl9Edb3mFAt6i+wPOLoqyuc2A1sXAHsWA/nHjdop2ZpRsEOIuSiP7Dh7AN4hwvVFG3c/wFVpNMbIXDvtfN01bs5JCBPht7n9EE1nY9mWu1eBu0pbr8N7A55thOuPhY1NCOWNWt6urMe3/1wz7U1cPIDQHopyQzWQtQ449DmwJgVYPQQoyTHtPVsBg4KdoUOHory8vNn1iooKDB061Ng+EWL7pBKgVOmXYJuO1r81VznXTnmeUQnN9l8sQaNE8Qk0NtgbU/pEYO7gjtg8rx+2zO9PgY4tarYLa4Qw/RCIgwODN1USDa765yoKxbWmu0lJDlCoIb/OrZPA2hEU8OjJoGDnwIEDaGhoaHa9rq4OBw8eNLpThNi88jxAovQaseYpLBnlqSxJA1BVZHBTO87wn/vKqBgsebgbXhkZg8QIP5q6slXNgp3WsV5HWe8of4zsGiwv1zVK8emuS6ZpnGWBLXOBpjrN9erEwJZ5NKWlByd9Kp8+fVr+9/Pnz6OoSPELTSKRYOfOnWjXrp3pekeIrbKl9ToyqouUy24APqF6N1PXKMH+iyXysrebE/p1DDC2d0RodWLgRoaiLIoAAmOF64+AXh0Vg70XiuWjl3+cLMB/+rdHfDvdFuarVZDJjdzo4tYJ7nT0sGTj7tlK6BXs9OjRAwzDgGGYFqer3N3d8dVXX5msc4TYrGY7sToL0w99qB5lcXg5d8RFWLJeU3Dpl26jpkFxOvSwuCC4ONHyQJt3ZS8gVVqMGz3C+qdmzaR9gCem9W2PNYdyAXADLK/8fhoDOwdAXNcEkbszhsUFITHcV79RzIvb9evIhW0U7OhIr2AnNzcXLMuiQ4cOOHbsGNq2VZwZ4uLigsDAQDg6OmpogZBWwpa2nQPc/P+xb/nXLu3kvkITgfGpOn+K33GmkFceFW/FC7OJ7lQP/rSzU8719ezQTvgtq0CeAfzcrQqcu6XImJ564Cq6h4nw6cQE3den1Zbr1wl967diegU7kZHcMLeUTmIlRLNmwU4HYfqhi5IcbsFjnZrDO2ULImfu0hrw1DdJsDdHMYXl6eKI+zvTFJbNk0qAy7sVZWdPIHKAcP2xAr4eLni8dzi+SVe/G+t0gRiPpmbovvPQ3Ve/TuhbvxXTK9hRdunSJRw4cAAlJSXNgp+3337b6I4RYtOU1+x4BHBbu62RbEGkukBHRrYgcvY+jVMXGVfuolIp78jQ2CC4OdNor80ryARqSxXljkO4U7pbMZZlcejKXa31KuqasGjTKWyZ31/7lFaX0dwWc13FjNG9bitnULCzevVqzJ07FwEBAQgODub9BzIMQ8EOad0aawFxvqJszVNYJl4Q+XezKaxgNTWJTbm0g19uZVvOW3Iyvxxnb2r5kHDPqQIxsvPLkRih5UNPWDI3bazLazK0J9AuSaf7EwODnffffx8ffPABXnnlFVP3hxDbV5oLQGlLqDUHOyZcENkokSItp1hednN2wOAubVusS2yM6nqdzhTspJ0v1l5Jye7zxdqDHYbh1sdpmlYGADcRMH5lq10gbgiDtkiUlZVh4sSJpu4LIfbBlg4ANeGCyKPXSlFe0ygvD44OhIeLwTPlxFqU3QBKzivKoT0Bbzq8VbYw2eT1A2O59XGhiS0/7uAMTN/earf9G8qgYGfixInYvXu39oqEtEa2tBPLhAsi/z6rMoXVjaaw7ILqqE50696FJSNydzZf/cBYYPZ+YNZeYMALgJdScClt5B/ZQXRi0MeuTp064a233sKRI0fQrVs3ODvz/xOfe+45k3SOEJtkSwkFTbQgUiJlsfucIsmoi6MDhsYEGts7Yg1a+RER6gyLC0LqgavaK94zPE7P0TCG4aaMw5K50bRfpyoeO/oNED9Bv/ZaOYOCnVWrVsHLywvp6elIT0/nPcYwDAU7pHXjjewwgH+UYF3RykQLIjOvl+JOleJ4jPs7B8DbTb9PvsQK1VcB15WOAPIOAUIShOuPFUkM90X3MBFOF2hfpJwQJkKPcF/DbxYzmstYLc7jyvlHgZsngHY9DW+zlTFoGis3N1ft17VrJj4BlhBboxzsiMIBZ3fh+qKNbEGkm5Y091oWRO44yz8LayTtwrIP1w7wz3hrxVmTVTEMg08nJsDHTfOYgY+bEz6ZmGDceXAOjkDv2fxrR78xvL1WiHK4E2JKtWVAzR1F2ZoXJ8toWxDpE6oxoaBUymKnUrDj5MBgmL5D9sQ6NdtyTut1lEUHeeO3uf3QPUz9h4VpfSN1z6CsSc+pgLOHonz2D6DS8MN6WxuDprFmzpyp8fG1a9ca1BlCbN5dlZFNa16vo0y2IPJmFnByA5C1TvFYm84ad35kF5SjqEJxSnPfjm3g6+Fizt4SS5BKgUtKG1Gc3ICoQcL1x0pFB3njz/n9kZ1fjt3ni3GxqBL7LiiyiG8+eQvPp0TD2dHIsQV3PyDhMSDz3vurtJH7+5DXjWu3lTB467nyV0lJCfbt24c//vgD5eXlJu4iITbElnZiqZItiBzzOeATpriefxRoqlf7NDoLy04VngSqFW/aiBoEuHior9+KMQyDxAg/vDIyBmumJyNBaX3OzfJa/Jl9yzQ36jOHX85cq/G1SRQMGtnZvHlzs2tSqRTz5s1Dhw5WfAYQIeZmy8GODMMAUfcDp37myk11QMFxoH3zs5BYluWt13FggOFdaQrLLjTbck67sHTBMAzmD+6I//6QJb+28sAVPJzYDo4ORq53atsF6DgUuLqPK1ffBs7+DvSYYly7rYDJ1uw4ODjghRdewOef67GNlRB7Y0sJBTWJGsgv5x5ssdq5WxUoKKuVl3tH+SPAy9WcPSOWcpGOiDBUSmwQooO85OVrt6t5qRmM0mcuv3wklTvjjmhk0gXKV69eRVNTk/aKhNgr5WDHwRnwjRCuL8Zofz+/nPtPi9Wan4VFU1h2oeIWUHRaUQ7uBojC1NcnPA4ODOYN5o/qrth/BawpgpJOKfwR46LTQN6/xrdr5wyaxlq4cCGvzLIsCgsLsX37dkyfPt0kHSPE5rAsP6Ggfwduy6gt8g3n+l96b8F1wXGgoRpw8ZRXYVn+LiyAtpzbDcqabLQx3UOwLO0S8kprAHCjoOmXbmNwFyOTbTo4AL2fBnYsUlw7kgpE9jOuXTtn0MjOyZMneV+nT3OfAD777DMsX77clP0jxHZUFgKN1YqyLa7XUaY8lSVtBPKO8B6+VFyFa3cU329SpB+CfNws1TtiTs2yJlOwoy8nRwfMGcSfxl65X/eMyxr1eBxw9VGUL2wDyvNM07adMmhkZ//+/abuByG2z17W68hEDQSy1ivKuf8AnR6QF5tPYdGojl1oqOGSCcp4tuWyZxO9PZLUDl/svYTiCm7H1LHrpTiWW4reUf7GNezqDSROBY58zZVZKXBsNTD8/4zssf0yas3O7du3cejQIRw+fBi3b982VZ8IsU32sBNLmZZ1OzSFZaeuH+R24Ml0HsFNnRC9uTo5Yvb9/B3KX++/oqa2nnrPBqC0u+vEd9xUM2mRQT/B1dXVmDlzJkJCQjBw4EDcf//9CA0NxVNPPYWamhpT95EQ26B6AGhAZ2H6YSpegUBbpWSChdlAHXcO0NXbVbhYXCl/qHuYCGF+lIPFLtDBnyb1eO8I+HkozolLv3QbZ29qP09LK/8ooMuDinKdGDi10fh27ZRBwc7ChQuRnp6OrVu3ory8HOXl5fjzzz+Rnp6OF1980dR9JMQ22NvIDsBft8NKgRsZAGhUx26xLH9xsqML0HGIcP2xA56uTvhPf/5hwCsPmGh05z6VJINHv6Vt6GoYFOz8/vvvWLNmDUaNGgUfHx/4+PjgwQcfxOrVq/Hbb7+Zuo+E2AblYMfVh1vrYOua5dvhprJ2nKUt53ap6AxQcVNRbj+AWx9CjDK9b3t4uSqWyO44W4QrJVXGN9z+fiAoXlG+c1GRcJDwGBTs1NTUICioeZbUwMBAmsYirZOkESi7rii36Wgfp0O37w/euoDcf5BfWoOzNyvkl2KCvREV4Nn8ucT20JZzsxB5OOPJ+yLlZZYFUg+YYGcWwwB9nuZfO/qt8e3aIYOCnb59++Kdd95BXZ1iEVttbS3effdd9O3b12SdI8RmlOcBUqWEmvYwhQVwhw+GJCjKxWex/8R5XhUa1bEjqqecdx4uTD/s0FMDouDqpHjL3ZJ9E/mlJhgc6DYRcFfa3XV5V/P1g8SwYGf58uXIyMhAWFgYHnjgAaSkpCA8PByHDx/GF198Yeo+EmL97HG9jkwUf1dW8ek9vPKD3Wi9jl2oKuFOvZdpG8MtgiUm0dbbFZN7hcvLEimL1QevGd+wszuQ/B/+NRrdacagYKdbt264fPkyli5dih49eqB79+748MMPceXKFXTt2tXUfSTE+tl1sDOIVwwuPS7/e8e2nugcRGs67MLl3fwyTWGZ3H8HdoCT0mGgG4/no6SyTsMzdNRrFuCglDYv+0f5zknCMSip4NKlSxEUFITZs2fzrq9duxa3b9/GK6+8YpLOEWIz7C2hoLKI+7hfpPem6fo5nJM/RFNYdoSyJptdmJ8Hxie2w29ZBQCAhiYp1hzKxWujYrU8UwufUCBuHHcCOgA0VAEnfwT6zjOyx/bDoJGdb7/9FjExMc2ud+3aFd98843RnSLE5qgGO/52FOy4egPtkuTFjg6FCEIpAGAUTWHZh6Z64KpSZnx3PyCsl3D9sWNzBnXk7V3Y8O8NiGsajW9Y9TT0Y98CUonx7doJg4KdoqIihIQ0/0TXtm1bFBYWtvAMQuyc8oJAryDAzUd9XVuksgW9r8N5RPh7IC7Ezr7P1ur6IW40QKbzcMDRoIF/okWnQC/e0SrVDRJ89+914xsO78X7UIKy681317ViBgU7ssXIqg4fPozQ0FCjO0WITWmo5ucmsaf1OjIqwU4/h3MYFR8Mxh6215MWtpxT1mRzmjeY/zti7eFcVNc3qamtB9XRnaOpxrdpJwwK3WfNmoUFCxagsbERQ4cOBQDs3bsXL7/8MmVQJq1PqcqOCntaryMT1hsNcIYLuOH2fo7nEN3NTtbrsCxQkAlc3A7UlgPuvkCX0UBYsn3kStKGZfnrdRhHoOMD6usTo8W3E2Fwl7Y4cJE7U7K8phE/H8vDLJVztPQWNw7Y/SZQdS/Dee4/QPE5IIg2DhkU7Lz88ssoLS3FvHnz0NDQAABwc3PDK6+8gtdee82kHSTE6tnzTqx7yhoccEHaGX0duBw7YcwdtPMsA+AraL+MVpIDbJkL3DrJv37ocyA0ERifCgQauXjUWsmCvBPfA+U3FNcj+nIBHzGr+UM6yYMdAFj1zzVM7RsJVydHwxt1cuF2Zu1/X3Ht6DfA2K+M6Kl9MGgai2EYfPTRR7h9+zaOHDmCU6dOobS0FG+//bZe7SxduhS9evWCt7c3AgMDMX78eFy8eJFXh2VZLF68GKGhoXB3d8fgwYNx7tw5Xp36+no8++yzCAgIgKenJ8aOHYuCggJDvjVC9NcKgp2088U4LOF/OmSuHxSoNyZSkgOsHdE80JG5dZJ7vCRH/7ZZFsg/DuxZDGxdwP2Zf9x6zi0qyQFWDwHWpAAnv+c/VpZr2PdM9NKrvT96t1ckAyyprMfvWTc1PENHyf8BHF0V5dO/AtV3jW/XxhkU7Mh4eXmhV69eiI+Ph6urq/YnqEhPT8f8+fNx5MgRpKWloampCcOHD0d1teKY+o8//hjLli3DihUrcPz4cQQHB2PYsGGorFScuLxgwQJs3rwZGzduxKFDh1BVVYUxY8ZAIqGV6MQCVLOV2mGws+NsITKkKkPh987Jskksy43oaMtFUicGtszTL0hRDiQOfQ5kreP+XJPCXRc6kNAW5FXcNDzII3qZP5T/u+Kb9KtokkiNa9QzgMuqLNNUB5xYb1ybdsCoYMdYO3fuxIwZM9C1a1ckJCRg3bp1yMvLQ1YWl8WTZVksX74cb7zxBiZMmID4+Hh89913qKmpwU8//QQAEIvFWLNmDT777DOkpKQgMTERGzZswJkzZ7Bnzx5NtyfENJRHdhgHwK+9YF0xB3FtIw5duYPTbAdUs0ofanL/sZ6RCn0VZKp/s1d16wSXpK3sOlBfqfl7NudokSmYM8gjehvYOQDx7RQ7GvNKa7DttAl2NKuel3Xsf9z5fa2YoMGOKrGYewH6+3NDe7m5uSgqKsLw4YrzWVxdXTFo0CBkZGQAALKystDY2MirExoaivj4eHkdVfX19aioqOB9EWIw5WDHNwJw0n+U05rtu1CMRgmLJjjhmFQpv1ZVMXDnknAdM8bF7frV/3M+8EUCsDQMeD8Q+CwGSO0PfPcQsGkGsP1FYN8HwI+PWncgoW+Qp3x8BDE5hmEwX2Vn1soDVyCVGvmzEdIdiOyvKFfeAnL+Mq5NG2c1wQ7Lsli4cCEGDBiA+HjuyPqiIm5FueoJ60FBQfLHioqK4OLiAj8/P7V1VC1duhQikUj+FR4e3mI9QrSqKQVqyxRle5zCOqN4HdnNVFZtueHPlTQAlYVA8Vnu+z+3GTj+P+CfjwGxjmsFhQok9A3yLmwzTz+I3IiuwejY1lNevlRchT05xcY33GcOv3ykdSf8tZpg55lnnsHp06fx888/N3tMNZcHy7Ja83toqvPaa69BLBbLv/Lz8w3vOGnd7HxxcnV9E9IvKXaMnHPpzq+Qm27hHpmINew2EiKQUA7MdapfbpZuEAUHB6ZZ3p2vD1wFa+zIX8xoQBShKBcca9UjdVYR7Dz77LP466+/sH//foSFhcmvBwdzWSZVR2hKSkrkoz3BwcFoaGhAWVmZ2jqqXF1d4ePjw/sixCB3LvPLdhbs7L9YgvomxYLJiLj7ADeRosL1Q4DUyAWVQugyWr/6XR8Buj8GdBoGhPYEfCMBFyMPQLV0IHH7kv4Zda0hKGwFxvYIRTtfd3n5VH45Dl8xcgeVgyPQm39+ZWse3RE02GFZFs888wz++OMP7Nu3D1FRUbzHo6KiEBwcjLS0NPm1hoYGpKeno1+/fgCApKQkODs78+oUFhbi7Nmz8jqEmI09HwAKYMdZ/geNkd3bAe3vV1yoLeOmc2xNWDIQ0EW3uqE9gUfXABO+BZ78DfjvfmDBaeD1AuDNEmDhBWDOYSD+Ef36UHYdkJgga642kkbg4GfANwO46Td9xIwxT58Ij7OjA+YM4icU/Hr/FTW19dBzKuDsoSif2wxUtry8w94JGuzMnz8fGzZswE8//QRvb28UFRWhqKgItbW1ALjpqwULFmDJkiXYvHkzzp49ixkzZsDDwwNTpkwBAIhEIjz11FN48cUXsXfvXpw8eRJPPvkkunXrhpSUFCG/PdIaNAt2OgvTDzOoa5Rg/4USednbzQn9OgY0OzrCJtftNNVzX9q4iYDxK9VnUnZyBXxCgOD45qn6tbm2H1g9GMg7qt/z9FF4Glg9FNj7HiDR4ftVFtqTf9YSMauJyeEI8FJsbvj32l1k3dBz2lGVux+Q8LiiLG0EMtca16aNEjTYSU1NhVgsxuDBgxESEiL/+uWXX+R1Xn75ZSxYsADz5s1DcnIybt68id27d8PbWzGE/Pnnn2P8+PGYNGkS+vfvDw8PD2zduhWOjkZkoiREF8o5dpzcAJ92wvXFxNIv3UZNgyJX1bDYILg4OdhHsJP+EVB+XXOd0J7AzF26Z1AOS+ayLuuj6Aywdji3O6vqtvb6umqqB/b+H5fXp+g0/zEnD8DZveXnyWgL8ojJuTk7Yvb9/NmN1AMmGN1RXaic8SXw17PWl+jSzBjW6FVQtq+iogIikQhisZjW7xDdSaXAkhAuaRcABHYF5rWc7sAWvfBLNjafVGR0XT0tGcPigrhfjp92BqrvvTm7eAGvXAccnYXpqL5ungD+lwKwSklHU94F6soVZ2PFjOFGNfR9s5fl2dG0/dzBEZC2kPDUVQQMfRNInmncieP5x4G/ngFuX2j+WOcRwJjPgfqKlo/JALggb/xK+z0mw4pV1Teh39K9qKhTTG9OSg6Dk6MDRO7OGBYXhMRwX/0P4F0zHMhXM4Jo48ei6Pr+bcQripBWruKmItAB7Gq9Tn2TBHvOK7a/ero44v7OAVyBYbh1O+f+4MoNVcCtbCC8l+U7qq+mei5njnKgE/8IMGCBadoPjOVGgzQFEuO+5g5n3P0Gl6tIpl4M7FjEHd/w4GdARB/97t1QA+x7HziyEoDKZ1h3P2DUx1xmXYYB0A6YvZ/bnXNhm/FBHjEJL1cnzOgfhS/3KjY+/JqpSGeQeuAquoeJ8OnEBEQH6bhAviRH87o6WaJLfUYxbRAFO4QYyo63nWdcuYvKesWnyyExgXBzVpoWjhqoCHYA4Po/thHs/PMpUHJeUfYIAEZ9Ytp7BMZqDySC4oDoEdx02pFUfvAlm9rq8SSQshjwastd13Q6+/WD3NRE2fXm/YkbDzz4CeAVyL/OMNxzw5JN+/0TowzsFMALdlSdLhDj0dQM/Da3n/aAR5Yxu6Facz1ZosvZ++w20KVghxBD2XGws+Msf9fOg91C+BVaWrdz/4tm7pWRCk8Dh5bxr43+FPBsY/p76RJIuPkAIz4AejwB/P0ScOMw//HsDcCFrcDQt7iTyP96puXT2T0CgJo7zdv3CgJGfwbEPmT890MsgmVZvLf9vNZ6FXVNWLTpFLbM7695SsuQjNl2GvxSsEOIoezsAFCWZXEyvxy7zhZhS/Yt+XVXJwaDu7TlV/bvAPiEARX3htjzjnBTRNZ6VIakEfhzHiBV2uodNw7o+rBwfZIJigNmbAfObAJ2v8mf2qoTc4EQ48gf/VHWUqDT4wkukHL3a/4YsVon88txukDLcSP3nCoQIzu/HIkRGv6PDcmYbafBjlUkFSTEJtnRyM6l4kqM+/owJqzMwLf/XEODUiJBVydHFJTV8p/AMPzRnaY6oOC4hXprgEOfc9NDMu7+3LoYa8EwQPdJwDOZwH3zueBGmbpAR5VPGPDk79wCYwp0bE6a0jo5XezWVl/fxJV2nDGbgh1CDKUc7Lj5Ah7+gnXFGJeKK/FoaobaT5QVdU14NDUDl4or+Q/Yyhb04nNA+sf8aw9+olgLY03cfICRS4A5B4EIA5Kijl8JdKL8YrZKXKvfyeRa6+ubAduOM2ZTsEOIIZoagPIbinKbTja5sI9lWby06RRvq2tLZGsEeJkqou7nV7LGYEfSxC28lCq9KXQZrX+2Y0sL6gr8528geqR+z7u23zz9IRYhctcvfYPW+voei2LHGbMp2CHEEGXXAVbpTCgbncIyZI2AnCiMW7sjU5CpfdeHpWV8CRRmK8puvsCYZbYRmDIM4B2ivZ4yO56GaA2GxbV8nqM6w7XV1yfRpZ1nzKZghxBD2Ml6HaPXCChPZUkbuYXK1uL2ReDAUv61kR8C3sHC9McQNA3RqiSG+6J7mEh7RQCxId7oEe6ruRLDcAkD3XRos9+ztvEhwEAU7BBiCDs5ANToNQLWum5HKuGmryQNimudhwMJjwnXJ0PQNESrwjAMPp2YAB837RulpSxQr7SRQC1ZokttIzz/ruCywtspCnYIMYSdjOwYvUagvZWu2/n3a+BmpqLs6gOMWW57n1xpGqLViQ7yxm9z+2kd4blYVIm3/zwLnU58kiW6nLUXGPACkPQf7uBaV6XjFW5mASd/MLL31ovy7BBiCNUcO8prV2zIsLggpB64qr3iPc3WCHgFAoFxiqzEhdmK7L5CuXMF2P8B/9qIDwCRDR7SKpuG0HbeFh3caVeig7zx5/z+yM4vx+7zxRDXNkLk7gwHBvh6v+L1+mtmAbqH+eLJ+yK1N9pSosugrlyySpk9i7kklDa6s1QTGtkhxBDKIzveoYCrl3B9MYI+awQSwkQtrxFQHt1hpcANAQ9DlUq4s6+UzyzrOBRInCpcn4ylbRpC39PZiU1gGAaJEX54ZWQMljzcDa+MjMGiETF4ok8Er967W88h83qpYTfp8QQQpnTMS20psPc9I3ptvSjYIURf9ZVAVZGibKPrdQDFGgEnB80jAj5uTvhkYkLLqelV1+1cP2jCHurp2CogX2mRtIs38NCXtj/i0dI0xIAXuPLsfRTotCLvPNQVSZGKhJGNEhZzNpxAkbhOw7PUcHAAHvwUYJRCgaz1wM0TxnfUylCwQ4i+7OyYCGdHBzRJ1c/7J4SJNB862L4/AKVgQqh1O6XXgD3v8q8Nfw/wDRemP6Ymm4ZIWQw8tJz7MyzZ9gM5ohcXJwekPtETgd6Ko1nuVNVj7o9ZqG/SMdO2stAeQPJTShdYYPuLdrdYmYIdQvRlJ4uTZb7/9zqv3DPCF1P6RGDu4I7YPK8ftszvr/l0ZXc/ICRBUS4+C1S3cF6TOUmlwJ/PAk1Kx1pEDeRGQAixM4E+bkh9siecHRWB7sm8ciz+S/shoi0a+gZ3oKzMrRPAye+N7KV1oWCHEH3ZUbBTXd+E3zIL5GVHBwZfP9FTvkYgMcJP86nKMkJPZWWuAW4cUpSdPYGxX9GoB7FbSZH+WDy2K+/az8fy8NPRPP0bc/cDhqmMiu5ZDNQYuBbIClGwQ4i+7CjY+eNEASrrFUdFjOwajBCRu/4NRQ3ily05lVV2A0h7h38tZTHg195yfSBEAFN6R+CxXvxp2nf+OousG2X6N5YwBQjrrSjXlgF731Vf38ZQsEOIvpSDHQcnwE+HbZ9WiGVZfPfvDd616f3aG9ZYxH3cv4WMOYMdlgXyj3OfPLcuAL4bAzQqHVMR2R/oNct89yfESjAMg3fHdeXtkmyUsJi7IQslFXouWHZwAEarLlb+DijIMk1nBUbBDiH6YFn+AmW/9oCjfon5rMXhK3dxpaRKXo4N8UGv9n4anqGBqxc/od3dK0DFLSN72IKSHGD1EGBNCnDocyBrHVCuNGzv6MpNXznQrzbSOrg6OeKbJ5MQ4KVYsFxSWY95P55Agy4ZlpWFJKh8UGCBv1/kUjrYOPqNQIg+qm8D9RWKsg1PYa3PuM4rz+gXqdv6HHWaHR1h4nU7JTlccr1bJ9XXYRj+ERGEtALBIjesfKInL4VE5o0yvLftnP6NDVFdrHwSOPGdCXopLAp2CNGHnazXyS+twd4LikM9fT2cMa6HkRmGzXlOFssCW+ZqziIMcMkEt8zj6hPSivSO8sfbD8Xxrm04kodfjuu5YNndFxj+f/xre94Fqu8a10GBUbBDiD7s5ADQH47c4MUDk3uFw83Z0bhGw3pz00gyuemmCzoKMjWP6Ci7dYI754eQVmbqfZGYmBTGu/bWlnPIzi/Xr6HujwHh9ynKdeXA3sXGdk9QFOwQog87GNmpbZDgl+P58rIDw/2SNJqzGxDRR1EW5wNl141vFwAubtev/oVtprkvITaEYRj83/h43hEwDRIp5vyQhduV9bo31NJi5RPfcxsDbBQFO4Toww6yJ2/JvglxbaO8nBIbhDA/D9M0bq6prNpy89YnxE64OXMLltt4usivFVXUYf6PJ9Ao0WPBcnA3oPd/+ddseLEyBTuE6EN5ZMfZA/AOEa4vBmBZFt81W5jc3nQ3aG+mYMfVR7/6Qp66TojAQn3d8fUTPeGotGD52PVSPL/xJD7aeQGvbz6Dj3ZewIm8MrCappoHvwZ4tlWUC09xZ2fZIAp2CNGVVMKdvyTTpqPNZeg9mluKC0WV8nJ0kBf6dmxjuhu068llL5a5ftD4dTt1YuDGYf2eEzPGuHsSYuPu69AGb47mHxD795kipB64ip+O5iH1wFVMWJmBcV8fxqXiypYbcfcFhqksVt77nuWPgzEBCnYI0ZU4n7+t2QansFRHdab1bW/cdnNVjs5AZD9FuaoYuHPJ8PZKrwH/GwbczNT9OaE9+Tl/CGmlZvRrj5SYQI11TheI8WhqhvqAJ+ExIKKvolxXziX0tDEU7BCiKxtfnHyrvBa7zyu2m3u7OeHhRCO3m7fEVOt2rh8GVj8A3Lmo+3PcRMD4lTY34kaIuRRVas+kXFHXhEWbTrU8pcUwwIOfAozSbs2TP9jcYmUKdgjRlY0vTt5w5AYkUsUvs0nJ4fB0ddLwDAM1C3bS9W/j5Abg+3FArcpBhL3/C4Qmtvyc0J7AzF1AYGzLjxPSypzML8fZmxXaKwI4VSBWv0U9OL75YuXtC21qsbIZftMRYqdseGSnrlGCjUrbzRkGmNbXTGd6BXcD3Hy54W6Ay6Qslep2hINUAux5B8j4in/dyR14OBXo+jC3BuhmFre9vLacW1cQM4abuqIRHULk0pRGcnWx+3wxEiPUHBkz5DXg7O9AdQlXLjoNZK4Fes82speWQcEOIbpSDXb8OwjTDwNsPXULpdWK9UZDugQiso2nhmcYwcERaD9AkeumrhwoPsOdu6NJfSXw+2zg0g7+de8Q4LGfuMXPABfQhCVzX4QQtZRTTBhd300EDH8f2Kw0wrPv/4C48YBXW7VPsxYU7BCiK+Vgx6MN4OEvXF/0wJ1ufp13zeDTzXUVNZCf2C/3oOZgpzwP+PlxoPgs/3pIAvD4RsAn1Dz9JMSOidz1O6RYa/3uk7it53kZXLlODPw5HwiKU4yydhnNfRCxslFWWrNDiC4a64ByxTSQLU1hncgr483bdwjwxP2dAjQ8wwT0WaScfwxYPbR5oBM7FvjPDgp0CDHQsLggveoHKp2c3iKGuZdZWWmx8uVdwKHPgax13J9rUoDVQ7iDe60IBTuE6KIsF4DSTgUbCnbWZ9zglaf1jYSDg5k/dbWN4Scju3EYkLQwRH76V2D9GO40eWUDFwETvwNczDTVRkgrkBjuyzs6Qpv3t5/Hn9k3NVcK6gp0m6i5zq2TwNoRVhXwULBDiC5s9ADQ4oo67DhTKC97ujjiEZWDAs2CYfijOw1VwK1sRVkqBfb+H/DHbECidGaPoyswYTUw9E3dFjQTQtRiGAafTkyAj5tuK1YkLPD8xmysPZSrvhLLAiXntTdWJwa2zDPdYcBGot8mhOjCRndi/Xg0D01K280fTQqDt5t+8/gGU53K2vEyl4ws9x/g12nAwU/5j3u2BWZs49YFEEJMIjrIG7/N7ad2hKdLkDcCvFx4197bdh4f7bzQct6dgkxuJ5Yubp3gdk5aAVqgTIgubDDYaWiS4qejebxr08y9MFmZt8pam1snuK9DnzevG9gVmLIR8I2wTN8IaUWig7zx5/z+yM4vx+7zxRDXNkLk7ozhcUHoEe6LQnEdpq09hislVfLnpB64ijuV9Vg6oRucHJXGRS5u1+/mF7ZZxc5JCnYI0cUd5WCHsYlt53+fKcSdKsUU0f2dA9CxrZdlbl6SA/wxS7e60aOAR1YDrt7m7RMhrRjDMEiM8Gsxj06orzs2Pd0XM787jpN55fLrm7IKUFrdgBVTesLd5d6i5NryZs/XSN/6ZkLTWIToQnlkRxQOOLsL1xcdrTfn6eaasCywZS43Z6+NVxAweQMFOoQIzM/TBT/O6oMhXfg5c/ZeKMHUNUdRXnMvT5e7r34N61vfTCjYIUSb2jKgRumUXxtYnHwqv5yX+j3C3wODu2g+ENBkCjK53Ri6qCoGCrPN2h1CiG48XJywaloyJvTkn5mXeaMMk779F4XiWi6Pjj5ixpiwh4ajYIcQbe5e45dtYL1O89PNI+Fo7u3mMobM6RNCrIKzowM+m5iApwfyp+ovFVfhkZUZuOLSRf35dKpCe3LHuFgBCnYI0cbGFiffqarHttOK7ebuzo6YmBxuuQ7Y6Jw+IYTDMAxeezAWrz8Yw7t+S1yHR789gpw+H3HHR2ji5AaMX2k1mZQp2CFEGxsLdn4+mocGiVRefrhnO73TxhvFRuf0CSF8/x3YEcsmJcBJaVS4vKYRE34rw9GhP4PVNMLDOPITiwqMgh1CtLGhhIKNEik2HOVnTJ7et71lO2Gjc/qEkOYm9AzD6unJcHdWHBFR2yjBlM3lGFj6FsbXv4eVTWPxY9NQ5EuVjqFprAb2LxGgxy2jYIcQbZSDHQdnq84Fs+tcEYorFNvN+3Zogy7BFt7pFJZsk3P6hJCWDekSiB9n94Gvh2KEWMIC+eV1yGY74eOmx/BG0yzManwJElYxCsRmrbOaIyMo2CFEE5YF7l5VlP07AA6O6usLTHVhstlPN28JwwDjU7XP6buJrGpOnxCiXs8IP/w2py9CfNQfFnqRjcBGyVB5mWGlYHe9YYnuaUXBDiGaVBZxw7EyVrxe5+xNMY5fL5OX2/m6IyXWQtvNVQXGAjN3qR/hCe3JPR4Ya9l+EUIM1inQG++Nj9dYZ1nTo6hgFXnImKt7gctp5u6aVpRBmRBNbGi9juqozpP3RfLTvFtaYCwwez93Ns6FbdyuK3dfbo1OuyQa0SHEBp1QyrDckrsQ4eum8XjN+WfFxV1vAB0GA44W3CihgoIdQjSxkZ1YpdUN+PPULXnZ1ckBj/Wy4HZzdRiGW8NjBWfjEEKMJ65t1FpnnWQknnDcgwiH29yFOxeBrPVA79nm7ZwGNI1FiCY2EuxsPJ6HhibFdvNxPULh5+mi4RmEEKI/XdJYNMAZS5um8C/uX8JloxcIBTuEaKK8OBmwymCnSSLFhn9VtpsLsTCZEGL3hsUF6VRvh7Q3jkqVkhLWlgL/fGqmXmlHwQ4hmiiP7Lh4A14CLfhtAcuyOJFXhvk/ncAtcZ38enKkL7qGatkJRQghBkgM90X3MF1+vzDY4PNf/qWj3zb/AGkhFOwQoo6kCSjLVZTbdLSaRbWXiisx7uvDmLAyA7vOFfMeK6lswKXiSoF6RgixZwzD4NOJCfBx077kN1sShYaukxUXpI1A2ttm7J16FOwQok75DUDapChbyRTWpeJKPJqagdMF4hYfzyutwaOpGRTwEELMIjrIG7/N7ad1hCe/rBYv3B0L1tlDcfHCNiD3HzP3sDkKdghRxwrX67Asi5c2nUJFXZPGehV1TVi06RRYlrVQzwghrUl0kDf+nN8fm+f1w9zBHTGlTwTmDu6IRSO68Optv85gj99j/Cfveh2QSizYW9p6Toh6VrgT62R+udoRHVWnCsTIzi9HYoSfmXtFCGmNGIZBYoRfs98xDAN8vPOivPxc3gAc99kGr4YS7kLRGSD7J6DnVIv1lUZ2CFHHChMKpp0v1l5JyW496xNCiLHmDuqISclh8nIt3PBW1SP8Svv+D6i33FQ7BTuEqGOFIzu6JPQypj4hhBiLYRi8P74b+nZoI7+2RdofZ9gOikpVxcCh5RbrEwU7hKhiWSD/OHfMgYy7H+Bq4dPDW6BLQi9j6hNCiCm4ODngmyeT0KGtJwCAhQPebXiSX+nfFUB5vkX6Q8EOIcpKcoDVQ4A1KUBDleJ6bRl3vSRHuL5B94ReMsP1rE8IIaYi8nDGuhm94H8vm3smG4Ntkj6KCk11wJ7FFukLBTuEyJTkAGtHALdOtvz4rZPc4wIGPInhvogO8tKpbkKYCD3Cfc3bIUII0SCyjSdWTU2Cy71DiT9sehz1rNLeqLO/cSPpZkbBDiEAN3W1ZS5Qp2WnU50Y2DKPqy8AhmHQqa32YMfHzQmfTEwAYyVJEAkhrVdye398MrE7AKCADcRaySh+hV2vmf13qqDBzj///IOHHnoIoaGhYBgGW7Zs4T3OsiwWL16M0NBQuLu7Y/DgwTh37hyvTn19PZ599lkEBATA09MTY8eORUFBgQW/C2IXCjLVj+iounWCv57HgsqqG7DvYonGOglhIvw2tx+ig4RfY0QIIQAwrkc7vJASDQD4umkcbrM+igcLjgNnfzfr/QUNdqqrq5GQkIAVK1a0+PjHH3+MZcuWYcWKFTh+/DiCg4MxbNgwVFYqtqstWLAAmzdvxsaNG3Ho0CFUVVVhzJgxkEgsm7CI2LiL2/Wrf2GbefqhxS+Z+ahrVJxuPqRLW15Cr83z+mHL/P4U6BBCrM5zD3TCw4ntUAUPLGuayH8w7R2gsdZs9xY0qeCoUaMwatSoFh9jWRbLly/HG2+8gQkTJgAAvvvuOwQFBeGnn37C008/DbFYjDVr1uCHH35ASkoKAGDDhg0IDw/Hnj17MGLECJP0k2VZnMwvR9r5YohrGyFyd8awuCAkhvvSNIG9qC03b30TaJJI8YPK6eYLh3VBN50O5SOEEGExDIMPH+mGm2W1+OX6EExz3I1Yh3u7sSoKuN1ZAxeZ5d5Wu2YnNzcXRUVFGD58uPyaq6srBg0ahIyMDABAVlYWGhsbeXVCQ0MRHx8vr9OS+vp6VFRU8L7UUT5wMfXAVfx0NA+pB65iwsoMjPv6MJ0/ZC/cfc1b3wT25BTjZrnik09ypB8FOoQQm+Lq5IhvpyYhoo0X3m/ib0WXHlwGVBaZ5b5WG+wUFXHfcFAQf+tsUFCQ/LGioiK4uLjAz89PbZ2WLF26FCKRSP4VHh7eYj1tBy6eLhDTgYv2osto/erHjDFPPzRYd/g6r/yf/lEW7wMhhBjLz9MFa2f0wlnXntgjSZRfd2isQWPae2a5p9UGOzKq00Qsy2qdOtJW57XXXoNYLJZ/5ec3T2pEBy62MmHJQGii9noAENoTaJdk3v6oOH+rAkdzS+XlEJEbhnelHDqEENvUoa0Xvp2ahI+lT6KRdZRfdzz9E85lHcRHOy/g9c1n8NHOCziRV2b0e6zVBjvBwcEA0GyEpqSkRD7aExwcjIaGBpSVlamt0xJXV1f4+PjwvlQZcuAisWEMA4xPBZzcNNdzEwHjV3L1Lei7jOu88pP3RcLZ0WpfvoQQotV9HdrgvxNGYoMkRX7NASykW56B96H30TXrHXgf+gD/l/odxq04ZNQsitX+toyKikJwcDDS0tLk1xoaGpCeno5+/foBAJKSkuDs7MyrU1hYiLNnz8rrGIoOXGyF2sYA3iHqHw/tCczcBQTGWq5PAEqrG7Al+6a87OLkgMd7R1i0D4QQYg6PJoWh+r6XUM56yq91c7yOeU5b8YTTXsxz+gubXd/B/91+Dq+k/mJwwCPobqyqqipcuaI4bDE3NxfZ2dnw9/dHREQEFixYgCVLlqBz587o3LkzlixZAg8PD0yZMgUAIBKJ8NRTT+HFF19EmzZt4O/vj5deegndunWT784yFB242AoVnQHKchVlz0AgZjS3GDlmDDd1JcDuu5+P5aG+SbHdfHyPUHn6dUIIsXVzRyVjfeYwPIUtauskOFzDevYtvPGzO756foreO6EFDXYyMzMxZMgQeXnhwoUAgOnTp2P9+vV4+eWXUVtbi3nz5qGsrAx9+vTB7t274e2tyCHy+eefw8nJCZMmTUJtbS0eeOABrF+/Ho6Ojs3upw+9D1x0E/SfkpjC6V/45QEvAH3nCdOXexolUmw4wt9uPr1fe2E6QwghZnCqoBy9pKe0zjWJmBrMKv0U2XmjkBjpr9c9GJZW1qKiogIikQhisVi+fudEXhkmrFS/fV1V/05tsHpaMjxcKOixSVIJ8HlXoLKQKzMOwMILgLewi4C3ny7E/J9OyMu9o/zx69N9BewRIYSY1g+bNmHquVk61/++6xpMm/gogJbfv1titWt2hJYY7ovueuQwOXzlLh5J/Rf5pTVm7BUxm+sHFYEOAHQYInigAwDrM3J55Zn92wvTEUIIMZPw2wf0qh9xe7/e96BgRw2GYfDpxAT46DE9lVNYgbErDiHj6h0z9oyYxelf+eXuk4Tph5KzN8U4fl2x07CdrztSYoUPwAghxJREqDZrfYCCHY2ig7zx29x+akd4urcT4ZHEdrxrZTWNmLrmGNYdzqXcO7aisRY4/5ei7OwhSNJAVetVtptP7RsJJ9puTgixMyH3Us2Yqz4g8AJlWxAd5I0/5/dHdn45diudjTU8Lgg97p2N1b9zAF794wwa7u2YkUhZvLv1PM7dqsD74+Ph5mzcYmliZhd3AA1K2xljRgOuXsL1B8Cdqnr8lX1LXnZzdsBjvVrO9E0IIbYsqNcE4HSqHvUf0fseFOzogGEYJEb4ITHCr8XHJ/QMQ6dALzz9QxYKxXXy679lFeBySRW+fTIJwSItyeqIcFSnsLoJP4X189E8NEgU280fTmwHXw/abk4IsT9MWC/UtU2A2+1TWuvWBSbALSxZ73vQmLiJdA/zxV/PDECv9vyA6FR+OR5acQhZN0rVPJMIqvoucEWRlBIeAUDHIerrW0CjRIofaLs5IaS1YBi4TfwWEhfNm4IkLiK4PfqtQfnOKNgxobbervhx1n148j5+dtvblfV4bNURbDyWJ1DPiFrnNwNSpfPP4h8BHPXLsWRqO84WoaSyXl7u26ENYoLVb6kkhBCbFxgLx1m7wKo5o5AN7QnHWYZnsKdpLBNzcXLA++O7oWuoCG//eRaNEm6RcqOExat/nMG5WxV4a0wcnB0ZnMwvR5rSOqBhcUFIvLcOiFiIFe7CWn+Yv938P7TdnBDSGgTGgpm9H7iZBVzYBtSWyzPYM0ZmsKdgx0we7x2BzoFemLPhBO5UKT6l/3DkBk7mlUHKAucLK3jPST1wFd3DRPh0YgKig7xVmySmVpoL5B9VlP07WPw0c1Wn8stxIq9cXg7zc8cDtN2cENJaMAwQlsx9mRBNY5lRcnt/bHt2ABJUtq6fvVXRLNCROV0gxqOpGUad7kp0dGYTv9x9siBnXylTPd18et/2cHSgkT5CCDEGBTtmFixywy9P98UjPcN0fk5FXRMWbTpFeXrMiWVb2IU1UZi+3FNSWYetpxXbzd2dHTEpmbabE0KIsSjYsQA3Z0d8OrG7Xqn+TxWIkZ1fbrY+tXq3TgJ3LyvK7ZKBNh2F6w+An4/my9d4AcCEnu0g8hB2sTQhhNgDCnYshGEYuOqZXHD3+WIz9YY0X5g8WZh+3NPQJMWGo/zt5jNouzkhhJgEBTsWJK5tNGt9oiNJE3D2d0WZcQTiJwjXHwB/nynEbaXt5gM6BaAzLVInhBCToGDHgkTu+k1J6Fuf6Cj3AFBdoih3egDwDBCsOwCwTmVhMm03J4QQ06Fgx4KGxem3hTjvbg2q65u0VyT6sbIprJN5ZTiltD4rso0HhnQJFK5DhBBiZyjYsaDEcF+1J6i3ZPuZQoxY/g8OXr5txl61Mg3VQM42RdnFC+jyoHD9QfPTzaf1bQ8H2m5OCCEmQ8GOBTEMg08nJsDHTfdcjgVltZi65hgWbToFcQ2t4THahb+BxmpFOWYM4OIhWHeKK+qw/XShvOzh4oiJybqnKSCEEKIdBTsWFh3kjd/m9lM7wtM11Aej4oObXd+UVYCUz9Ox82xhC88iOjv9C78s8PEQPx7NQ5NUsd380aQw+LjRWi1CCDElOi5CANFB3vhzfn9k55djt9LZWMPjgtDj3tlYh6/cwat/nEZ+aa38ebcr6zFnwwmMig/Gu+O6ItDbTcDvwgZV3Qau7lOUvYKAqEGCdae+SYKfVLabT+vbXpjOEEKIHaNgRyAMwyAxwg+JEX4tPt6/UwB2LRiIz3ZfwtrDuVBOprzjbBEyrt7FW2Pi8EjPdnRwqK7O/QGwEkU5/hHAUbiXwLZThbhT1SAvD4xui06BXoL1hxBC7BUFO1bMw8UJb42Jw+juIXjlt9O4XFIlf0xc24iXNp3CX6duYcnD8Qjz8wDLsnSSuiZWNIXFsmyzhcm03ZwQQsyDYekAJlRUVEAkEkEsFsPHx0fo7rSovkmCr/dfxcr9V3hrPABuUet/+rXHP5fv4MxNcbPn0knqAO5cAVYonWgeEA3MPybYwZ9ZN0rxSOq/8nJUgCf2LhxEu7AIIUQPur5/0wJlG+Hq5IiFw6Kx9dkBzRY31zRI8PWBqy0GOgCdpA4AOKOaW2eSoCecrzt8nVee3jeSAh1CCDETCnZsTGyID/6Y2w+vPxgDVyfd//ta9UnqVnbCeaG4FjvOFsnLXq5OeCSJtpsTQoi5ULBjg5wcHfDfgR2xa8FAxIXoPu3Wak9SL8gEynIV5fD7AL/2gnXnxyN5kKhsN/em7eaEEGI2FOzYsPYBnhgYrd+ZTq3yJHUrWJjMsixO5JXhg+3nsfrgNd5j0+l0c0IIMSvajWXjKur0Ozur1Z2kLmnktpzLODgBXR+2aBcuFVfipU2ncLqg+ZoqbzcnNEqkFu0PIYS0NjSyY+PoJHUtru4Dau4qyp2HAx7+Frv9peJKPJqa0WKgAwCVdU20eJwQQsyMgh0bp+9J6sP1rG/zBJzCYlkWL206pXX0rVUvHieEEAugYMfG6XOSuruzA9q3Ee7QS4urr+QO/pRx8QaiR1rs9ifzy9WO6KhqtYvHCSHEAijYsXH6nKRe2yjFtLXHW8/p6TnbgCbF2WKIGwc4u1vs9ml6LgZvlYvHCSHEAijYsQPaTlJXduamGNPWHm0dC5UF3oWl779xq/g/IYQQAdBuLF2wLJer5eJ2oLYccPcFuowGwpIFzcKrTNNJ6pnXS/HB3xfkdU8ViDFt7TH88FRv+NhrfpeKQiA3XVH2DgHaD7BoFzxdHPWq3+oWjxNCiIVQsKNNSQ6wZS5w6yT/+qHPgdBEYHwqEBgrTN9UqDtJPTHCDyyAJcoBT345pq89hu9n9rbPhHZnfwdYpS3d3R4FHPQLPoxR2yDBsdxSvZ7T6haPE0KIhdA0liYlOcDaEc0DHZlbJ7nHS3Is2y8D/HdgR7w6KoZ37WQeF/BU1euXq8cmNDsLa7LFbl3T0ISZ64/jlI6LkwEgIUyEHuG+5usUIYS0YhTsqMOy3IhOnZY3rDoxsGUeV9/KzRnUEYtGdOFdO5FXjhn2FvCUXAAKTynKgXFAULxFbl1V34Tpa4/h32t3tVe+x8fNCZ9MTABjJVOihBBibyjYUacgU/2IjqpbJ4CbWebtj4nMH9IJLw2P5l3LvFGG/6w7hmp7CXhUR3W6TbTI2ipxbSOmrjmK49fLeNcnJoehe7uWF48nhInw29x+iA7yNnv/CCGktaI1O+pc3K5f/QvbuAXLNuCZoZ0hZYFlaZfk145fL8N/1h/H+v/0goeLDf9YSKXAmU38axY44by8pgHT1h5rllfn2aGdsHAYF1y2tHi8R7gvjegQQoiZ2fC7mpnVlpu3vsCee6AzJFIWX+y9LL92LLcUM9cfx9oZNhzw5B8FyvMU5cgBgG+4WW9ZWt2AJ/93FOcLK3jXFw6LxnMPdJaXW1o8TgghxPxoGksdd1/z1rcCC1I647mhnXjXjlwrxVPrM1HbIBGoV0ZqllvHvKM6tyvr8fiqI80CnVdGxvACHUIIIcKhYEedLqP1q+9ue5/YGYbBC8Oi8cwQfsDz77W7mPX9cdQ12ljA09QAnNusKDu6cFmTzaSkog6PrfoXF1UO8XxzdCzmDu5otvsSQgjRj43OVVhAWDKXR0fXRcppb3OJ7Ia9Czi5mrdvJsQwDF4cHg0JyyL1wFX59cNX7mLWd8fxzNDOSL90W77OZFhcEBKtbZ2JLOljxpdAXbnieufhZgtCC8W1mLL6KHLvVPOuvzeuK6b1bW+WexJCCDEMw9JRy6ioqIBIJIJYLIaPj4/iAVmeHW3bz5WF9AAmrgP8O5i8n+bEsiw+3HkB36Zf01q3e5gIn05MsI4dROqSPgKAXxTw+M8mT/qYX1qDKf87gvxSxblbDAMsebgbHu8dYdJ7EUIIUU/t+7cKmsbSJDAWmLmLG+FpSdtYwDeSf60wG/hmIJfB14YwDINXR8bgvwO1B2mnC8R4NDUDl1SmbyxOW9LHslyTJ33Mu1uDx1Y1D3Q+fqQ7BTqEEGKlaGQHOkSGLMvl0bmwTXE2VswYoF0S0FAN/L0IOPVT8+clzQBGfmjRk7aNJZVK0XvJXtypatBaNyFMhC3z+wszpcWywOohuk0zhvYEZu8zOtfOtdtVmLL6KIoq6uTXHBjg88k9MK5HO6PaJoQQoj9dR3ZozY4uGIZbw9NSHh1XL+DhVCBqILD9RaBRaQ1H1nog/zg3rdW2S/PnWqHsArFOgQ7AHSianV8uzHZqQ5I+6pgHiWVZnMwvR5pSTpzYYG+8t+0879/G0YHBl48lYnT3EEO+A0IIIRZCwY6p9HicG+n57T9A8VnF9ZJzwKrBwOjPgB5TBOuertLOF+tVf/f5YmGCHTMlfbxUXImXNp1qlhxQlbMjgxVTemJE12D9+kEIIcTiaM2OKbWNBmbtAZJn8q831nCLaP94GqivEqZvOhLXNupXv0a3USCTM0PSx0vFlXg0NUNroOPkwODbqUkU6BBCiI2gkR1Tc3YHxnwOtL8f2Po8UK+UbO70Rm46ZeI67mDKgkxuhEK2DqjLaG70QcBt3SJ3Z73q/3P5Dq7drkKHtl5m6lELWJafJVkXWpI+siyLlzadQkWd9vPBIvw9MKRLoH73J4QQIhhaoAzdFzjprTSXm9ZSXVvi4Ax4BwPi/ObPCU0ExqeafLu0rk7klWHCygy9nuPi6ICnB3XA/CGd4ObsaKae3VNZBPz5DHAlTb/nzdqrcRpL3+9787x+dPQDIYQIjLaeWwP/KGDmbuC++fzr0saWAx2AC4xMvF1aH4nhvuge1vIJ3eo0SKT4at8VDPs8HXtz9Fvzo5ezvwMr79M/0Antya2n0uDP7Ft6Nblbz7VNhBBChEPBjrk5uQAjlwCP/Qy46hhE1ImBLfO46RoLYxgGn05MgI+b5hlONycHuDvxf3zyS2vx1HeZmP19JgrKakzXqZpS4LeZ3FdtmUqHtYwkuYmA8StbnBpslEix61wRZn2Xie8zruvVJX3XNhFCCBEOBTuWEvMg96arK9l2aQFEB3njt7n91I7wJISJ8NezA7Bv0WCM7tZ823Xa+WKkLEvH1/uvoKFJalxnLqcBK/s2T9Lo4Aw88A7w9D/qkz6G9uSSQqpMCV4sqsT7287jviV78fQPWdiTUwx9w0p91zYRQggRDq3ZgRnX7Kjasxg49Lnu9aMGAiOWAkFddVu0LDsjykSLnlmWRXZ+OXYr5ZsZHheEHipnY/1z6Tbe/vMsrt9tPprTsa0n/m9cPPp1CpC3qZrDpsXztuqrgN1vcLmKVAXFAw9/CwTHc21Kpbh44gDKTmyGQ70YUlcR/Ho+jC49B4Nx4OJ5cU0j/jp9C5sy87XuttIFrdkhhBDh6fr+TcEOLBjsbF0AZK3T/3megUDHIUCHIdyf3i1sedZ0RpQFFj3XNUqw6p9r+Hr/FdS3MJozNiEUj/cOx9IdF1oMNnjnbd3IADbPAcpv8CsxDkD/BcDgV+WHrWrKi9OtnQiP9w7Hv9dKsetckcZRJl93Jzg5Olh/5mhCCCFyFOzowWpHdtQJjFMEPpH9uG3Y2g4sdRO1OKVjanl3a7B46znsu1Ci93MD3KTYnXAQ/qdWAaoTS/4dgPHfABF95JdkeXF02S7eEgcGGNwlEBOTwvBAbBCu363W2p6PmxN+m9vPOg5BJYSQVo6CHT1YLNjJPw6sSTFtmw7O3ChHgw7JCvU9I8rAaTGWZZF2vhjvbj2Pm+W1qo8ikbmCYY5ZEKEaYngiTZKEBjhhmXMqujgUNG+w1yywKe+iwdEd9U1S1DdKUd8kwVPrM3HRgMNIO7T1xKTkcExIbIdAHzfeY5pGihLCRPjEWk57J4QQQsGOPiwW7OhzeGVgHNDjSSD3AHD9EJeF2RS05JuRM8G0WE1DE77adwWr/rkKiRTozBTgU+dvkOBwrVldKcuNtCgrhj/eYufigKSb0Qud3Z0dMT4xFI8mhaNnhK/GKShd1yoRQggRFgU7erBYsANwQYS+U05N9UD+MeDafuDqPuBWNppN8+jKtz3QOQXwjQB8I7k//doD7n6K0RpD+qjBq7+fRlbmv/jNZTFEjG5B2x+SAVjcOA0VME1m5tn3R+GN0XEmaYsQQoh1oFPPrVVgLBckqB016cltUVcOIpxcgaj7ua8H3ubyzuSmA1f3c1uydZnCkim/Dhz/X/PrLl5c4COKAG5laQ50AO7xzXOAqVsAVgpIm7hkidImQCoBJLK/NyGiJgeznJfrFOg0sQ54pvFZ7JT20VpXH9UNEpO2RwghxHZQsCOEwFhg9n4uj86FbYr1MDFjuEy/2qZKPPyBrg9zX+7+wGETLHpuqAJKznNfuirMBj5ur7XaPEDnjE5OjBRlTm0R5ukOFycHuDo53vtT+csRl0sqcalY9yCP8uIQQkjrRcGOUBiGWzujy/oZTWJGmybYsSJf9LiJ4AnPaqyj71lWw+OCjO0WIYQQG2U3GZRXrlyJqKgouLm5ISkpCQcPHhS6S5YRlqw+g7Cq0J7AomvAfw8Ak74Hhr8P9JoNdB4BtI0BnD3M2lVdBTnXaa2jzxleCWEi9Aj3NbJXhBBCbJVdjOz88ssvWLBgAVauXIn+/fvj22+/xahRo3D+/HlEREQI3T3zYhhuZ5QuC4rHrwQ823BfLQVILAvsfBU4+o3u9/fvwGV4dnAGHJy4L0cnxd8dnIH8I3odfcG4+2qvc+8ML13y4nwyMYF2URFCSCtmF7ux+vTpg549eyI1NVV+LTY2FuPHj8fSpUu1Pt+iu7HMReNW8RYWPaujby4gXbaym6PNeygvDiGEtF6tZjdWQ0MDsrKy8Oqrr/KuDx8+HBkZLa/pqK+vR319vbxcUVFh1j5ahLGLnmVk02K65AIK7cm1LUSb90QHeePP+f0pLw4hhBC1bD7YuXPnDiQSCYKC+AtQg4KCUFRU1OJzli5dinfffdcS3bMsUyx61ndaTJdgwhxt8ppnkBjhRwdzEkIIaZHdLFBW/QTPsqzaT/WvvfYaxGKx/Cs/P98SXbQdslxA6hY+h/bU/5wtc7RJCCGE6MDmR3YCAgLg6OjYbBSnpKSk2WiPjKurK1xdXS3RPdtlqmkxc7dJCCGEaGHzwY6LiwuSkpKQlpaGhx9+WH49LS0N48aNE7BndsBUuYDM3SYhhBCigc0HOwCwcOFCTJ06FcnJyejbty9WrVqFvLw8zJkzR+iuEUIIIURgdhHsTJ48GXfv3sV7772HwsJCxMfH4++//0ZkZKTQXSOEEEKIwOwiz46x7CLPDiGEENLK6Pr+bTe7sQghhBBCWkLBDiGEEELsGgU7hBBCCLFrFOwQQgghxK5RsEMIIYQQu2YXW8+NJduQZhcHghJCCCGthOx9W9vGcgp2AFRWVgIAwsPDBe4JIYQQQvRVWVkJkUik9nHKswNAKpXi1q1b8Pb2Vnt4KMBFkOHh4cjPzzdZPh5baNMW+miONm2hj+Zo0xb6aI42baGP5mjTFvpojjZtoY/maNMW+qhPmyzLorKyEqGhoXBwUL8yh0Z2ADg4OCAsLEzn+j4+PiZPPmgLbdpCH83Rpi300Rxt2kIfzdGmLfTRHG3aQh/N0aYt9NEcbdpCH3VtU9OIjgwtUCaEEEKIXaNghxBCCCF2jYIdPbi6uuKdd96Bq6trq2rTFvpojjZtoY/maNMW+miONm2hj+Zo0xb6aI42baGP5mjTFvpojjZpgTIhhBBC7BqN7BBCCCHErlGwQwghhBC7RsEOIYQQQuwaBTuEEEIIsWsU7Ohh5cqViIqKgpubG5KSknDw4EGD2/rnn3/w0EMPITQ0FAzDYMuWLUb1benSpejVqxe8vb0RGBiI8ePH4+LFi0a1mZqaiu7du8uTOvXt2xc7duwwqk1lS5cuBcMwWLBggcFtLF68GAzD8L6Cg4ON7tvNmzfx5JNPok2bNvDw8ECPHj2QlZVlcHvt27dv1k+GYTB//nyD2mtqasKbb76JqKgouLu7o0OHDnjvvfcglUoN7iPApVxfsGABIiMj4e7ujn79+uH48eM6P1/bzzXLsli8eDFCQ0Ph7u6OwYMH49y5cwa398cff2DEiBEICAgAwzDIzs42qo+NjY145ZVX0K1bN3h6eiI0NBTTpk3DrVu3jPq+Fy9ejJiYGHh6esLPzw8pKSk4evSowe0pe/rpp8EwDJYvX25UH2fMmNHs5/O+++4zqk0AyMnJwdixYyESieDt7Y377rsPeXl5BrXX0muIYRh88sknBvexqqoKzzzzDMLCwuDu7o7Y2FikpqYa9X0XFxdjxowZCA0NhYeHB0aOHInLly+rbU+X39/6vHZ0aU/f1462Ng157ejST31fO+pQsKOjX375BQsWLMAbb7yBkydP4v7778eoUaPUvmi1qa6uRkJCAlasWGGS/qWnp2P+/Pk4cuQI0tLS0NTUhOHDh6O6utrgNsPCwvDhhx8iMzMTmZmZGDp0KMaNG6fxzUlXx48fx6pVq9C9e3ej2+ratSsKCwvlX2fOnDGqvbKyMvTv3x/Ozs7YsWMHzp8/j88++wy+vr4Gt3n8+HFeH9PS0gAAEydONKi9jz76CN988w1WrFiBnJwcfPzxx/jkk0/w1VdfGdxHAJg1axbS0tLwww8/4MyZMxg+fDhSUlJw8+ZNnZ6v7ef6448/xrJly7BixQocP34cwcHBGDZsmPx8On3bq66uRv/+/fHhhx/q9g1qabOmpgYnTpzAW2+9hRMnTuCPP/7ApUuXMHbsWIPbBIDo6GisWLECZ86cwaFDh9C+fXsMHz4ct2/fNqg9mS1btuDo0aMIDQ3VWE/XNkeOHMn7Of3777+NavPq1asYMGAAYmJicODAAZw6dQpvvfUW3NzcDGpPuW+FhYVYu3YtGIbBI488YnAfX3jhBezcuRMbNmxATk4OXnjhBTz77LP4888/DWqTZVmMHz8e165dw59//omTJ08iMjISKSkpan8f6/L7W5/Xji7t6fva0damIa8dXfqp72tHLZbopHfv3uycOXN412JiYthXX33V6LYBsJs3bza6HWUlJSUsADY9Pd2k7fr5+bH/+9//jGqjsrKS7dy5M5uWlsYOGjSIff755w1u65133mETEhKM6o+qV155hR0wYIBJ21T1/PPPsx07dmSlUqlBzx89ejQ7c+ZM3rUJEyawTz75pMF9qqmpYR0dHdlt27bxrickJLBvvPGG3u2p/lxLpVI2ODiY/fDDD+XX6urqWJFIxH7zzTd6t6csNzeXBcCePHnSqD625NixYywA9saNGyZrUywWswDYPXv2GNxeQUEB265dO/bs2bNsZGQk+/nnn+vUP3VtTp8+nR03bpzObejS5uTJkw3+mdTl33HcuHHs0KFDjWqza9eu7Hvvvce71rNnT/bNN980qM2LFy+yANizZ8/KrzU1NbH+/v7s6tWrdWpT9fe3sa8dTe8Hhr52dHmP0fe1o0ub+rx2lNHIjg4aGhqQlZWF4cOH864PHz4cGRkZAvVKM7FYDADw9/c3SXsSiQQbN25EdXU1+vbta1Rb8+fPx+jRo5GSkmKSvl2+fBmhoaGIiorCY489hmvXrhnV3l9//YXk5GRMnDgRgYGBSExMxOrVq03SV4D7edqwYQNmzpyp8eBZTQYMGIC9e/fi0qVLAIBTp07h0KFDePDBBw3uV1NTEyQSSbNP3e7u7jh06JDB7crk5uaiqKiI9zpydXXFoEGDrPZ1BHCvJYZhjBrZU9bQ0IBVq1ZBJBIhISHBoDakUimmTp2KRYsWoWvXribpFwAcOHAAgYGBiI6OxuzZs1FSUmJwW1KpFNu3b0d0dDRGjBiBwMBA9OnTx+gpe5ni4mJs374dTz31lFHtDBgwAH/99Rdu3rwJlmWxf/9+XLp0CSNGjDCovfr6egDgvY4cHR3h4uKi8+tI9fe3sa8dU78f6Nqmvq8dbW0a89qhYEcHd+7cgUQiQVBQEO96UFAQioqKBOqVeizLYuHChRgwYADi4+ONauvMmTPw8vKCq6sr5syZg82bNyMuLs7g9jZu3IgTJ05g6dKlRvVLpk+fPvj++++xa9curF69GkVFRejXrx/u3r1rcJvXrl1DamoqOnfujF27dmHOnDl47rnn8P3335ukz1u2bEF5eTlmzJhhcBuvvPIKHn/8ccTExMDZ2RmJiYlYsGABHn/8cYPb9Pb2Rt++ffF///d/uHXrFiQSCTZs2ICjR4+isLDQ4HZlZK8VW3kdAUBdXR1effVVTJkyxegDDrdt2wYvLy+4ubnh888/R1paGgICAgxq66OPPoKTkxOee+45o/qkbNSoUfjxxx+xb98+fPbZZzh+/DiGDh0qf/PWV0lJCaqqqvDhhx9i5MiR2L17Nx5++GFMmDAB6enpRvf3u+++g7e3NyZMmGBUO19++SXi4uIQFhYGFxcXjBw5EitXrsSAAQMMai8mJgaRkZF47bXXUFZWhoaGBnz44YcoKirS6XXU0u9vY147pnw/0KdNfV87mto0xWuHTj3Xg+qncJZlDf5kbk7PPPMMTp8+bZJP4126dEF2djbKy8vx+++/Y/r06UhPTzco4MnPz8fzzz+P3bt3q52z19eoUaPkf+/WrRv69u2Ljh074rvvvsPChQsNalMqlSI5ORlLliwBACQmJuLcuXNITU3FtGnTjO7zmjVrMGrUKJ3WWajzyy+/YMOGDfjpp5/QtWtXZGdnY8GCBQgNDcX06dMNbveHH37AzJkz0a5dOzg6OqJnz56YMmUKTpw4YXCbqmzlddTY2IjHHnsMUqkUK1euNLq9IUOGIDs7G3fu3MHq1asxadIkHD16FIGBgXq1k5WVhS+++AInTpww6b/b5MmT5X+Pj49HcnIyIiMjsX37doMCCtli+XHjxuGFF14AAPTo0QMZGRn45ptvMGjQIKP6u3btWjzxxBNG/y758ssvceTIEfz111+IjIzEP//8g3nz5iEkJMSg0WdnZ2f8/vvveOqpp+Dv7w9HR0ekpKTwfldpoun3tyGvHVO+H+japiGvHU1tmuK1QyM7OggICICjo2OzCLqkpKRZpC20Z599Fn/99Rf279+PsLAwo9tzcXFBp06dkJycjKVLlyIhIQFffPGFQW1lZWWhpKQESUlJcHJygpOTE9LT0/Hll1/CyckJEonE6P56enqiW7duGnc+aBMSEtIsmIuNjTV4MbqyGzduYM+ePZg1a5ZR7SxatAivvvoqHnvsMXTr1g1Tp07FCy+8YPSIWceOHZGeno6qqirk5+fj2LFjaGxsRFRUlFHtApDvkrOF11FjYyMmTZqE3NxcpKWlGT2qA3A/m506dcJ9992HNWvWwMnJCWvWrNG7nYMHD6KkpAQRERHy19GNGzfw4osvon379kb3UyYkJASRkZEGv5YCAgLg5ORkltfSwYMHcfHiRaNfR7W1tXj99dexbNkyPPTQQ+jevTueeeYZTJ48GZ9++qnB7SYlJck/JBYWFmLnzp24e/eu1teRut/fhr52TP1+oEubhrx2tLVpitcOBTs6cHFxQVJSknwHjUxaWhr69esnUK/4WJbFM888gz/++AP79u0zyZuTuvsYOqz9wAMP4MyZM8jOzpZ/JScn44knnkB2djYcHR2N7l99fT1ycnIQEhJicBv9+/dvtv3x0qVLiIyMNLZ7WLduHQIDAzF69Gij2qmpqYGDA//l6+joaPTWcxlPT0+EhISgrKwMu3btwrhx44xuMyoqCsHBwbzXUUNDA9LT063mdQQofllfvnwZe/bsQZs2bcxyH0NfS1OnTsXp06d5r6PQ0FAsWrQIu3btMln/7t69i/z8fINfSy4uLujVq5dZXktr1qxBUlKSwWueZBobG9HY2Gi215JIJELbtm1x+fJlZGZmqn0dafv9re9rxxzvB7q0qe9rx9B+GvLaoWksHS1cuBBTp05FcnIy+vbti1WrViEvLw9z5swxqL2qqipcuXJFXs7NzUV2djb8/f0RERGhd3vz58/HTz/9hD///BPe3t7yTwAikQju7u4G9fH111/HqFGjEB4ejsrKSmzcuBEHDhzAzp07DWrP29u72Vysp6cn2rRpY/Bc8ksvvYSHHnoIERERKCkpwfvvv4+KigqjpnJeeOEF9OvXD0uWLMGkSZNw7NgxrFq1CqtWrTK4TYAb1l+3bh2mT58OJyfjXnoPPfQQPvjgA0RERKBr1644efIkli1bhpkzZxrV7q5du8CyLLp06YIrV65g0aJF6NKlC/7zn//o9HxtP9cLFizAkiVL0LlzZ3Tu3BlLliyBh4cHpkyZYlB7paWlyMvLk+fykL2xBgcHq823pKnN0NBQPProozhx4gS2bdsGiUQify35+/vDxcVF7zbbtGmDDz74AGPHjkVISAju3r2LlStXoqCgQG3qAW3ft+qbiLOzM4KDg9GlS5cW29PWpr+/PxYvXoxHHnkEISEhuH79Ol5//XUEBATg4YcfNqjNiIgILFq0CJMnT8bAgQMxZMgQ7Ny5E1u3bsWBAwcMag8AKioqsGnTJnz22Wdq+6VPm4MGDcKiRYvg7u6OyMhIpKen4/vvv8eyZcsMbnPTpk1o27YtIiIicObMGTz//PMYP358s00uMtp+f8vyken62tHl/UDf1462NpuamvR+7Whrs7q6Wu/Xjlp67d1q5b7++ms2MjKSdXFxYXv27GnUtu79+/ezAJp9TZ8+3aD2WmoLALtu3TqD+zhz5kz599u2bVv2gQceYHfv3m1wey0xduv55MmT2ZCQENbZ2ZkNDQ1lJ0yYwJ47d87ofm3dupWNj49nXV1d2ZiYGHbVqlVGt7lr1y4WAHvx4kWj26qoqGCff/55NiIignVzc2M7dOjAvvHGG2x9fb1R7f7yyy9shw4dWBcXFzY4OJidP38+W15ervPztf1cS6VS9p133mGDg4NZV1dXduDAgeyZM2cMbm/dunUtPv7OO+8Y1KZsG25LX/v37zeozdraWvbhhx9mQ0NDWRcXFzYkJIQdO3Yse+zYMYO/b1W6bD3X1GZNTQ07fPhwtm3btqyzszMbERHBTp8+nc3LyzO4TZk1a9awnTp1Yt3c3NiEhAR2y5YtRrX37bffsu7u7jr/XGprs7CwkJ0xYwYbGhrKurm5sV26dGE/++wzjWkhtLX5xRdfsGFhYfJ/yzfffFPja1OX39/6vHZ0aU/f1462Ng157Whr05DXjjrMvRsSQgghhNglWrNDCCGEELtGwQ4hhBBC7BoFO4QQQgixaxTsEEIIIcSuUbBDCCGEELtGwQ4hhBBC7BoFO4QQQgixaxTsEEIIIcSuUbBDCLEJgwcPxoIFC4TuBiHEBlGwQwghhBC7RsEOIYSo0dDQIHQXCCEmQMEOIcRmSKVSvPzyy/D390dwcDAWL14sfywvLw/jxo2Dl5cXfHx8MGnSJBQXF8sfnzFjBsaPH89rb8GCBRg8eLC8PHjwYDzzzDNYuHAhAgICMGzYMDN/R4QQS6BghxBiM7777jt4enri6NGj+Pjjj/Hee+8hLS0NLMti/PjxKC0tRXp6OtLS0nD16lVMnjzZoHs4OTnh8OHD+Pbbb83wXRBCLM1J6A4QQoiuunfvjnfeeQcA0LlzZ6xYsQJ79+4FAJw+fRq5ubkIDw8HAPzwww/o2rUrjh8/jl69eul8j06dOuHjjz82fecJIYKhkR1CiM3o3r07rxwSEoKSkhLk5OQgPDxcHugAQFxcHHx9fZGTk6PXPZKTk03SV0KI9aBghxBiM5ydnXllhmEglUrBsiwYhmlWX/m6g4MDWJblPd7Y2NjsOZ6enibsMSHEGlCwQwixeXFxccjLy0N+fr782vnz5yEWixEbGwsAaNu2LQoLC3nPy87OtmQ3CSECoWCHEGLzUlJS0L17dzzxxBM4ceIEjh07hmnTpmHQoEHyaamhQ4ciMzMT33//PS5fvox33nkHZ8+eFbjnhBBLoGCHEGLzGIbBli1b4Ofnh4EDByIlJQUdOnTAL7/8Iq8zYsQIvPXWW3j55ZfRq1cvVFZWYtq0aQL2mhBiKQyrOolNCCGEEGJHaGSHEEIIIXaNgh1CCCGE2DUKdgghhBBi1yjYIYQQQohdo2CHEEIIIXaNgh1CCCGE2DUKdgghhBBi1yjYIYQQQohdo2CHEEIIIXaNgh1CCCGE/P+GNRht7IyCUTAKRsEoGAWjYFgDAO01i2CAIpmRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##折线图(连接起来的散点图)图形绘制  横坐标 是 hour，纵坐标是 count,是否是工作日 分开绘制\n",
    "sns.pointplot(data=df6,x='hour',y='count',hue='workingday')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f4de7d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour_workingday\n",
       "21_1    311\n",
       "23_1    311\n",
       "15_1    311\n",
       "12_1    311\n",
       "22_1    311\n",
       "20_1    311\n",
       "16_1    311\n",
       "13_1    311\n",
       "14_1    311\n",
       "9_1     310\n",
       "11_1    310\n",
       "0_1     310\n",
       "5_1     310\n",
       "6_1     310\n",
       "19_1    310\n",
       "7_1     310\n",
       "10_1    310\n",
       "1_1     309\n",
       "2_1     305\n",
       "4_1     297\n",
       "8_1     290\n",
       "3_1     289\n",
       "18_1    255\n",
       "17_1    244\n",
       "10_0    145\n",
       "1_0     145\n",
       "6_0     145\n",
       "7_0     145\n",
       "0_0     145\n",
       "18_0    145\n",
       "11_0    145\n",
       "23_0    145\n",
       "19_0    145\n",
       "14_0    145\n",
       "13_0    145\n",
       "8_0     145\n",
       "22_0    145\n",
       "21_0    145\n",
       "9_0     145\n",
       "20_0    145\n",
       "15_0    145\n",
       "17_0    145\n",
       "4_0     145\n",
       "3_0     144\n",
       "16_0    144\n",
       "12_0    143\n",
       "2_0     143\n",
       "5_0     142\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #通过数据可以看出，是否是工作日 和 小时 这两个因素 对 用车数量 数据影响很大，因此新增属性 hour_workingday\n",
    "# #该 将 hour和是否是工作日 做 字符串的拼接\n",
    "def f(hour,workingday):\n",
    "    return str(hour)+'_'+str(workingday)\n",
    "#\n",
    "all_df['hour_workingday']=all_df.apply(lambda row:f(row['hour'],row['workingday']),axis=1)\n",
    "all_df['hour_workingday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fe021fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # 继续分析天气的影响\n",
    "import pandas as pd\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "all_df.groupby('weather').apply(mean_numeric_columns)['count'].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c1e1c5c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.385954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.385954</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           temp     count\n",
       "temp   1.000000  0.385954\n",
       "count  0.385954  1.000000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #温度和用车量的影响\n",
    "all_df[['temp','count']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "14770d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3236     28\n",
       "1067     10\n",
       "3245     26\n",
       "2300     28\n",
       "4716     21\n",
       "         ..\n",
       "9983     14\n",
       "6157      6\n",
       "10221    12\n",
       "1025      8\n",
       "168       6\n",
       "Name: temp_int, Length: 10739, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #新增 整数 温度列 ，将 温度 取整数，目的 是 便于 统计\n",
    "all_df['temp_int']=all_df['temp'].apply(lambda x: int(x))\n",
    "all_df['temp_int']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2a0926a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #统计 不同温度下 ，用车数量的 均值\n",
    "import pandas as pd\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "all_df.groupby('temp_int').apply(mean_numeric_columns)['count'].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d31533d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3236     74\n",
       "1067     65\n",
       "3245     78\n",
       "2300     24\n",
       "4716     48\n",
       "         ..\n",
       "9983     56\n",
       "6157     40\n",
       "10221    89\n",
       "1025     75\n",
       "168      74\n",
       "Name: humidity_int, Length: 10739, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #分析 不同湿度 的用车数量 均值\n",
    "# #1.对 湿度 取整\n",
    "all_df['humidity_int']=all_df['humidity'].apply(lambda x: int(x))\n",
    "all_df['humidity_int']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d7e577ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #2.分组 ，对 组内 求 count 均值，画 柱状图\n",
    "import pandas as pd\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "all_df.groupby('humidity_int').apply(mean_numeric_columns)['count'].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "daa3ae20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3236     19\n",
       "1067      6\n",
       "3245      6\n",
       "2300     19\n",
       "4716     11\n",
       "         ..\n",
       "9983      7\n",
       "6157     19\n",
       "10221     6\n",
       "1025     26\n",
       "168       7\n",
       "Name: windspeed_int, Length: 10739, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #不同 风速windspeed 对 用车数量count的影响 #\n",
    "# # 一.求均值\n",
    "#1.取整数\n",
    "all_df['windspeed_int']=all_df['windspeed'].apply(lambda x: int(x))\n",
    "all_df['windspeed_int']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2c636dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.分组 ，对 组内 求 count 均值，画 柱状图\n",
    "import pandas as pd\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "all_df.groupby('windspeed_int').apply(mean_numeric_columns)['count'].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7fc121b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3236     19\n",
       "1067      6\n",
       "3245      6\n",
       "2300     19\n",
       "4716     11\n",
       "         ..\n",
       "9983      7\n",
       "6157     19\n",
       "10221     6\n",
       "1025     26\n",
       "168       7\n",
       "Name: windspeed_int, Length: 10739, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 二.求和\n",
    "# #1.取整数\n",
    "all_df['windspeed_int']=all_df['windspeed'].apply(lambda x: int(x))\n",
    "all_df['windspeed_int']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a851676d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KAAAB5s91jFzDeG4cQQEAAM4hoAAAAOcQUAAAgHMIKAAAwDkEFAAA4BwCCgAAcA4BBQAAOIeAAgAAnENAAQAAziGgAAAA5xBQAACAcwgoAADAOQQUAADgHAIKAABwDgEFAAA4h4ACAACcQ0ABAADOIaAAAADnEFAAAIBzCCgAAMA5BBQAAOAcAgoAAHAOAQUAADiHgAIAAJxDQAEAAM4hoAAAAOcQUAAAgHMIKAAAwDkEFAAA4BwCCgAAcA4BBQAAOIeAAgAAnENAAQAAziGgAAAA5xBQAACAcwgoAADAOQQUAADgHAIKAABwDgEFAAA4h4ACAACcQ0ABAADOIaAAAADnEFAAAIBzCCgAAMA5BBQAAOAcAgoAAHAOAQUAADiHgAIAAJxT5oCydu1aXXfddYqPj5fH49HSpUt92s1MGRkZio+PV0REhNLS0rRz506fPkVFRRoxYoTq1aunGjVqqHfv3tq/f/8FPREAAFB5lDmgHD16VJdffrmeeeaZs7ZPmTJFTz31lJ555hlt2rRJsbGx6tatmw4fPuztM3LkSC1ZskQLFy7UunXrdOTIEfXq1UsnT548/2cCAAAqjZCy7pCenq709PSztpmZpk2bpvHjx6tfv36SpDlz5igmJkavvPKK7r77bhUUFGjmzJmaO3euunbtKkmaN2+eEhIStHLlSvXo0eMCng4AAKgMAnoNyt69e5Wbm6vu3bt7t4WHh6tTp05av369JCkzM1PHjx/36RMfH68WLVp4+/xSUVGRCgsLfRYAAFB5BTSg5ObmSpJiYmJ8tsfExHjbcnNzFRYWpjp16pTa55cmTZqkqKgo75KQkBDIYQMAAMdclLt4PB6Pz7qZnbHtl87VZ+zYsSooKPAu2dnZARsrAABwT0ADSmxsrCSdcSQkLy/Pe1QlNjZWxcXFys/PL7XPL4WHh6tWrVo+CwAAqLwCGlCSk5MVGxurFStWeLcVFxdrzZo1Sk1NlSS1bt1aoaGhPn1ycnK0Y8cObx8AAFC1lfkuniNHjmjPnj3e9b1792rr1q2qW7euGjVqpJEjR2rixIlKSUlRSkqKJk6cqOrVq+uWW26RJEVFRWnIkCEaPXq0oqOjVbduXY0ZM0YtW7b03tUDAACqtjIHlM2bN6tz587e9VGjRkmSBg0apNmzZ+vBBx/UsWPHdO+99yo/P1/t2rXT8uXLFRkZ6d1n6tSpCgkJ0U033aRjx46pS5cumj17toKDgwPwlAAAQEVX5oCSlpYmMyu13ePxKCMjQxkZGaX2qVatmqZPn67p06eX9eEBAEAVwG/xAAAA5xBQAACAcwgoAADAOQQUAADgHAIKAABwDgEFAAA4h4ACAACcQ0ABAADOIaAAAADnEFAAAIBzCCgAAMA5BBQAAOAcAgoAAHAOAQUAADiHgAIAAJxDQAEAAM4hoAAAAOcQUAAAgHMIKAAAwDkEFAAA4BwCCgAAcA4BBQAAOIeAAgAAnENAAQAAziGgAAAA5xBQAACAcwgoAADAOQQUAADgHAIKAABwDgEFAAA4h4ACAACcQ0ABAADOIaAAAADnEFAAAIBzCCgAAMA5BBQAAOCckPIeAAAALkh6eNmv9tk3+drfYCSQOIICAAAcREABAADOIaAAAADnEFAAAIBzCCgAAMA5BBQAAOAcAgoAAHAOAQUAADiHgAIAAJxDQAEAAM4hoAAAAOcQUAAAgHMIKAAAwDkEFAAA4BwCCgAAcA4BBQAAOIeAAgAAnENAAQAAzgl4QMnIyJDH4/FZYmNjve1mpoyMDMXHxysiIkJpaWnauXNnoIcBAAAqsItyBOWyyy5TTk6Od9m+fbu3bcqUKXrqqaf0zDPPaNOmTYqNjVW3bt10+PDhizEUAABQAV2UgBISEqLY2FjvUr9+fUk/Hz2ZNm2axo8fr379+qlFixaaM2eOfvzxR73yyisXYygAAKACuigBJSsrS/Hx8UpOTtaAAQP05ZdfSpL27t2r3Nxcde/e3ds3PDxcnTp10vr160utV1RUpMLCQp8FAABUXgEPKO3atdPLL7+sf/7zn3rhhReUm5ur1NRUHTp0SLm5uZKkmJgYn31iYmK8bWczadIkRUVFeZeEhIRADxsAADgk4AElPT1dN9xwg1q2bKmuXbtq2bJlkqQ5c+Z4+3g8Hp99zOyMbacbO3asCgoKvEt2dnaghw0AABxy0W8zrlGjhlq2bKmsrCzv3Ty/PFqSl5d3xlGV04WHh6tWrVo+CwAAqLwuekApKirSrl27FBcXp+TkZMXGxmrFihXe9uLiYq1Zs0apqakXeygAAKCCCAl0wTFjxui6665To0aNlJeXp0cffVSFhYUaNGiQPB6PRo4cqYkTJyolJUUpKSmaOHGiqlevrltuuSXQQwEAABVUwAPK/v37dfPNN+vgwYOqX7++rrrqKm3cuFGJiYmSpAcffFDHjh3Tvffeq/z8fLVr107Lly9XZGRkoIcCAAAqqIAHlIULF56z3ePxKCMjQxkZGYF+aAAAUEnwWzwAAMA5BBQAAOAcAgoAAHAOAQUAADiHgAIAAJxDQAEAAM4hoAAAAOcQUAAAgHMIKAAAwDkEFAAA4BwCCgAAcA4BBQAAOIeAAgAAnENAAQAAziGgAAAA5xBQAACAcwgoAADAOQQUAADgHAIKAABwDgEFAAA4h4ACAACcQ0ABAADOIaAAAADnEFAAAIBzCCgAAMA5BBQAAOAcAgoAAHAOAQUAADiHgAIAAJxDQAEAAM4hoAAAAOcQUAAAgHMIKAAAwDkEFAAA4BwCCgAAcA4BBQAAOIeAAgAAnENAAQAAziGgAAAA5xBQAACAcwgoAADAOQQUAADgHAIKAABwDgEFAAA4h4ACAACcQ0ABAADOIaAAAADnEFAAAIBzCCgAAMA5BBQAAOAcAgoAAHBOSHkPAAAA/DaSHl7mV799k6+9yCP5dRxBAQAAziGgAAAA55RrQHn22WeVnJysatWqqXXr1vrggw/KczgAAMAR5RZQFi1apJEjR2r8+PH6+OOPdfXVVys9PV1ff/11eQ0JAAA4otwCylNPPaUhQ4bozjvvVLNmzTRt2jQlJCRoxowZ5TUkAADgiHK5i6e4uFiZmZl6+OGHfbZ3795d69evP6N/UVGRioqKvOsFBQWSpMLCQp9+JUU/+vX4v9zvbKhVtlr+1qNW5ajlbz1qVY5a/tajVuWo5W+986l16t9m9us7Wjn45ptvTJJ9+OGHPtsfe+wxa9KkyRn9J0yYYJJYWFhYWFhYKsGSnZ39q1mhXL8HxePx+Kyb2RnbJGns2LEaNWqUd72kpETff/+9oqOjz9r/lMLCQiUkJCg7O1u1atW6oLFSi1oVaWzUoha1+P/bxVpmpsOHDys+Pv5X65VLQKlXr56Cg4OVm5vrsz0vL08xMTFn9A8PD1d4eLjPttq1a/v9eLVq1QrIZKUWtX6LetSiFrXcqRXoetSSoqKi/KpTLhfJhoWFqXXr1lqxYoXP9hUrVig1NbU8hgQAABxSbqd4Ro0apdtvv11t2rRR+/bt9fzzz+vrr7/WPffcU15DAgAAjii3gNK/f38dOnRIjzzyiHJyctSiRQu9/fbbSkxMDNhjhIeHa8KECWecHqIWtQJRK9D1qEUtarlTK9D1qFV2HjN/7vUBAAD47fBbPAAAwDkEFAAA4BwCCgAAcA4BBQAAOIeAAgAAnFOuX3UfaPv379eMGTO0fv165ebmyuPxKCYmRqmpqbrnnnuUkJBQ3kMEAAB+qDRHUNatW6dmzZppyZIluvzyyzVw4EDddtttuvzyy7V06VJddtll+vDDD/2q9fHHH2vv3r3e9Xnz5ulf//VflZCQoA4dOmjhwoV+j2vEiBH64IMPyvx8SjN9+nQNGjRIr776qiRp7ty5at68uZo2bapx48bpxIkTAXssXHz79+/XkSNHzth+/PhxrV271u86hw4d0qpVq/T9999Lkg4ePKjHH39cjzzyiHbt2lWm8Rw8eNC7/sEHH+jWW2/V1Vdfrdtuu00bNmzwu9aTTz6pr776yu/+qBqOHj2qF154QXfccYfS09P1hz/8QXfccYdefPFFHT16tEy1mPeVXEB+ntgBbdq0sZEjR5baPnLkSGvTpo1fta644gp7//33zczshRdesIiICLvvvvtsxowZNnLkSKtZs6bNnDnTr1oej8eCgoIsJSXFJk+ebDk5OX7tdzaPPPKIRUZG2g033GCxsbE2efJki46OtkcffdQmTpxo9evXtz//+c/nXf+XcnNz7S9/+UuZ9snOzrbDhw+fsb24uNjWrFlzXuMoLi62JUuW2JQpU2zu3Ll25MiRMo3nu+++866vXbvWbrnlFuvQoYPdeuuttn79+jKN5ccff7SZM2faHXfcYT179rRrr73Whg8fbitXrixTnQMHDljbtm0tKCjIgoODbeDAgT6vW25urgUFBflV66OPPrKoqCjzeDxWp04d27x5syUnJ1tKSoo1btzYIiIiLDMz069a7du3t7ffftvMzJYuXWpBQUHWu3dve+ihh+z666+30NBQe/PNN/2q5fF4LDg42Lp27WoLFy60oqIiv/YrzZEjR+z555+3wYMHW8+ePS09Pd0GDx5sL7zwQpnmxK9h3p8pUPN+586dFh8fb7Vr17Y+ffrYXXfdZUOHDrU+ffpY7dq17ZJLLrGdO3f6VauqzHszN+d+oOfY2VSagFKtWjX77LPPSm3ftWuXVatWza9a1atXt6+++srMfg4r//3f/+3TPn/+fGvevLlftTwej61cudLuv/9+q1evnoWGhlrv3r3tzTfftJMnT/pV45Tf/e539tprr5mZ2datWy04ONjmzZvnbV+8eLE1bty4TDXPZevWrX5/SAbyA7d9+/aWn59vZmZ5eXnWsmVLCwsLs5SUFKtWrZo1atTI9u/f73etQL3xZGVlWWJiokVHR1tcXJx5PB679tprrV27dhYcHGw33nijHT9+3K9aAwcOtKuuuso2bdpkK1assDZt2ljr1q3t+++/N7OfXy+Px+NXra5du9qdd95phYWF9sQTT1jDhg3tzjvv9LYPGTLE+vbt61etyMhI27t3r5mZtWvXziZPnuzTPn36dLviiiv8quXxeGzWrFnWp08fCw0NtejoaLv//vtt+/btfu1/ukB+sP0a5r2vQM77tLQ0GzBgwFk/tIuKiuzmm2+2tLQ0v2pVhXlv5u7cD+QcK02lCSjJycn20ksvldr+0ksvWXJysl+1oqOjbfPmzWZm1qBBA9u6datP+549eywiIsKvWh6Px7799lsz+/kvokWLFlmPHj0sODjY4uPjbdy4cZaVleVXrYiICG9wMjMLDQ21HTt2eNf37dtn1atX96uWmdknn3xyzmXRokV+T9ZAfuCe/poNHTrUWrVq5T3ydPDgQUtNTbV/+7d/86tWIN940tPT7e677/YGy0mTJll6erqZmX3++eeWlJRkEyZM8KtWfHy8ffTRR971n376yfr06WOtWrWyQ4cOlemDrU6dOvbpp5+a2c9zLCgoyKf2li1b7JJLLvGrVlRUlH3yySdm9vPcP/XvU/bs2eP3HDv9v+O3335rjz/+uDVt2tSCgoKsbdu29vzzz1thYaFftQL5wca8L795HxERcc4P0+3bt/v93loV5r2Zu3M/kHOsNJUmoPz973+3sLAwGzZsmC1dutQ2bNhgGzdutKVLl9qwYcMsPDzcZsyY4Vet2267zYYMGWJmZjfeeKP96U9/8mmfOHGitWzZ0q9ap0/W03311Vc2YcIES0xM9HtCJCcn2zvvvGNmP78xBAUF2auvvuptX7ZsmSUlJflV69TYgoKCzOPxnLGc2u7v2AL5gXv6a9akSRN76623fNpXrVrl9/MM5BtP9erV7fPPP/euFxUVWWhoqB08eNDMfv4rwt9x1ahRw6eWmdnx48etb9++9vvf/962bdvm9+tVo0YN7xuFmVnNmjXtiy++8K5/9dVXfh897N27tz388MNmZtajRw/729/+5tP+wgsvWEpKil+1Spv7a9eutUGDBlmNGjWsRo0aftUK5Acb87785n18fLwtXbq01PYlS5ZYfHy8X7Wqwrw3c3fuB3KOlabSBBQzs4ULF1q7du0sJCTE+6KHhIRYu3btbNGiRX7X+eabbywpKck6duxoo0aNsoiICOvQoYMNHTrUOnbsaGFhYbZs2TK/apU2WU8pKSmx5cuX+1Vr/PjxVr9+fbvzzjstOTnZxo4da40aNbIZM2bYc889ZwkJCfYf//EfftUyM6tXr57NnDnT9u3bd9Zl2bJlZfqQDNQHrsfjsby8PDP7eeL/8n/Offv2WXh4uF+1AvnGEx8f73NOOz8/3zwej/evoS+//NLvcbVs2dL+8Y9/nLH91GvWqFEjv1+vpk2b2nvvveddf+utt+zHH3/0rm/cuNEaNmzoV61PP/3UoqOjbeDAgfbXv/7Vatasabfddps99thjNnDgQAsPD7dZs2b5VSsoKOicc7+goMCef/55v2oF8oONeV9+837ChAkWFRVlTzzxhG3dutVycnIsNzfXtm7dak888YTVqVPH72sgqsK8N3N37gdyjpWmUgWUU4qLi+3AgQN24MABKy4uPq8a+fn59tBDD1nz5s2tWrVqFhYWZomJiXbLLbfYpk2b/K6TlJTk/UvjQp04ccIeffRR69Wrl/dw2oIFCywhIcGio6Nt8ODBZbpgqkePHvbXv/611PatW7f6fXg6kB+4Ho/H/vCHP9j1119vderU8Z7nPGXDhg0WExPjV61AvvEMGjTIOnXqZLt27bIvv/zS+vfv73MIc/Xq1ZaQkOBXrQcffNC6d+9+1rbjx49b7969/X69MjIybMGCBaW2jxs3zvr16+dXLbOf//IZMGCARUZGeoN+aGiopaam2pIlS/yu82vhvCwC+cHGvC+/eW9mNnnyZO+1LEFBQd6/3OPi4uzxxx/3u05VmPdm7s79QM6x0lTKgAL/LF682ObOnVtq+/fff2+zZ8/2q5Y/H7j+TvzBgwf7LKefxjIzGzNmjPXo0cOvWmaBe+P59ttv7aqrrvK+sSYlJdmWLVu87f/zP/9jTz/9tF+1jh8/bgUFBaW2nzhxwvbt2+f32M7l6NGj9tNPP5V5v5KSEsvNzb2goB9Igfpg+63nvb8B5WLM+/79+zs170/35Zdf2vr16239+vU+p2YuVElJiZkFZt4H4g6cX47rfLg4980C995aGo+ZWXnf6oyK78SJE/rxxx9Vq1ats7afPHlS+/fvV2Ji4gU/1tGjRxUcHKxq1aqVaT8zU15enkpKSlSvXj2Fhoae1+NnZWWpqKhITZs2VUhIpfquwwph7969ys3NlSTFxsYqOTm53MbCvA+MsLAwffLJJ2rWrBm1zsGluX+6QM2xX+LdFaXKzs7WhAkT9NJLL/1q35CQkFLfpCXpwIED+stf/uJXrV/z/fff+z2u0536ZuHTleU5npKSknLW7WWtdezYMWVmZqpu3bpq3ry5T9tPP/2kV199VQMHDqTW/9m1a5c2btyo1NRUtW/fXp999pmmTJmioqIi3Xbbbbrmmmv8qnN6rfbt26tp06b67LPP9Le//a3MtUJCQvTNN9/otddeu+Bav3yOl156acBrPfLIIxdcKyQk5LzHNWrUqLNuP3nypCZPnqzo6GhJ0lNPPUWts0hOTlbt2rU1Z84cZWVlKT4+XgMHDjzvb0nPz8/31oqLi9OgQYPOq5bH41FYWFjAxuV1wcdgUGmV5Z54apWt1u7duy0xMdF7yLZTp0524MABb3tZ7v6oCrXeeecdCwsLs7p161q1atXsnXfesfr161vXrl2tS5cuFhIS4nPBJLXcrOXxeKxVq1aWlpbms3g8Hmvbtq2lpaVZ586dqXWauLg473WMX375pcXFxVlsbKx169bNGjZsaFFRUbZr167zqhUbG+tErdIQUKqw119//ZzL1KlT/f4AoVbZavXt29d69epl3333nWVlZdl1111nycnJ3u+5KcuHd1Wo1b59exs/fryZ/XxheJ06dWzcuHHe9nHjxlm3bt2o5XitiRMnWnJy8hmBJiQkpMxfNlYVapn5XnQ7YMAAS0tLs6NHj5rZz7e19+rVy/74xz9W6FqlIaBUYee6J/70e+OpFfhaDRo0sG3btvlsu/fee61Ro0b2xRdflOnDuyrUqlWrlvcLDU+ePGkhISE+t75u377d7ztcqFV+tczM/vd//9eaNGlio0eP9l58fb4f3lWh1ulB4GzBpyy3U7taqzSV5scCUXZxcXF67bXXVFJSctZly5Yt1LpItY4dO3bGhYZ///vf1bt3b3Xq1Emff/45tUoRFBSkatWqqXbt2t5tkZGRKigooFYFqNW2bVtlZmbqu+++U5s2bbR9+3Z5PJ4yj6Wq1JLk3beoqOiM6+hiYmL03XffVfhaZ0NAqcJat259zg9Vj8cj8/MmL2qVrVbTpk21efPmM7ZPnz5dffr0Ue/evf2qU1VqJSUlac+ePd71DRs2qFGjRt717OxsxcXFUcvxWqfUrFlTc+bM0dixY9WtWzedPHmyTPtXtVpdunTRlVdeqcLCwjOC/ddff6169epV+Fpnw108VdgDDzxwzp83b9y4sVatWkWti1Dr+uuv14IFC3T77bef0fbMM8+opKREzz33HLX+z7//+7/7vMG3aNHCp/2dd97x+04SapVfrV8aMGCAOnTooMzMzAu+Fbuy1powYYLPevXq1X3W33zzTV199dUVulZp+B4UAADgHE7xAAAA5xBQAACAcwgoAADAOQQUAADgHAIKUIXNnj3b5zstzldaWppGjhx5wXUutqSkJE2bNs2vvh6PR0uXLr2o4wFQOgIKUIX179//vL8wrbLLyclRenq63/0DFfYA/IzvQQGqsIiICEVERJT3MJwUGxtb3kMAqjSOoACVzJtvvqnatWurpKREkrR161Z5PB498MAD3j533323br755jP+6s/IyFCrVq00d+5cJSUlKSoqSgMGDNDhw4e9fY4ePaqBAweqZs2aiouL05NPPnnGGJ599lmlpKSoWrVqiomJ0R//+EdvW1pamoYPH67hw4erdu3aio6O1p/+9Cefb+EtLi7Wgw8+qEsuuUQ1atRQu3bttHr1ap/HWL9+vTp27KiIiAglJCTovvvu8/kivby8PF133XWKiIhQcnKy5s+fX6bX8fRTPPv27ZPH49HixYvVuXNnVa9eXZdffrk2bNggSVq9erXuuOMOFRQUyOPxyOPxKCMjo0yPB8AXAQWoZDp27KjDhw/r448/liStWbNG9erV05o1a7x9Vq9erU6dOp11/y+++EJLly7VW2+9pbfeektr1qzR5MmTve0PPPCAVq1apSVLlmj58uVavXq1MjMzve2bN2/Wfffdp0ceeUS7d+/Wu+++q44dO/o8xpw5cxQSEqKPPvpITz/9tKZOnaoXX3zR237HHXfoww8/1MKFC7Vt2zbdeOON6tmzp7KysiRJ27dvV48ePdSvXz9t27ZNixYt0rp16zR8+HBvjcGDB2vfvn16//339Y9//EPPPvus8vLyLuCVlcaPH68xY8Zo69atatKkiW6++WadOHFCqampmjZtmmrVqqWcnBzl5ORozJgxF/RYQJV3QT81CMBJV155pf3Xf/2XmZn17dvXHnvsMQsLC7PCwkLLyckxSbZr1y6bNWuWRUVFefebMGGCVa9e3QoLC73bHnjgAWvXrp2ZmR0+fNjCwsJs4cKF3vZDhw5ZRESE3X///WZm9tprr1mtWrV8apyuU6dO1qxZMyspKfFue+ihh6xZs2ZmZrZnzx7zeDz2zTff+OzXpUsXGzt2rJmZ3X777XbXXXf5tH/wwQcWFBRkx44ds927d5sk27hxo7d9165dJsmmTp3qz0tokmzJkiVmZrZ3716TZC+++KK3fefOnd7X0czOeC0BXBiOoACVUFpamlavXi0z0wcffKA+ffqoRYsWWrdunVatWqWYmBg1bdr0rPsmJSUpMjLSux4XF+c98vDFF1+ouLhY7du397bXrVtXl156qXe9W7duSkxM1O9+9zvdfvvtmj9/vn788Uefx7jqqqt8ft21ffv2ysrK0smTJ7VlyxaZmZo0aaKaNWt6lzVr1uiLL76QJGVmZmr27Nk+7T169FBJSYn27t2rXbt2KSQkRG3atPE+RtOmTS/4Itbf//73Pq+LpAs+KgPg7LhIFqiE0tLSNHPmTH3yyScKCgpS8+bN1alTJ61Zs0b5+fmlnt6RpNDQUJ91j8fjvZ7F/PjprsjISG3ZskWrV6/W8uXL9ec//1kZGRnatGmTXwGhpKREwcHByszMVHBwsE9bzZo1vX3uvvtu3XfffWfs36hRI+3evds79kA6/bU5VfvUawMgsDiCAlRCp65DmTZtmjp16iSPx6NOnTpp9erV57z+5Nc0btxYoaGh2rhxo3dbfn7+Gbcqh4SEqGvXrpoyZYq2bdvmvRbklNP3P7WekpKi4OBgXXHFFTp58qTy8vLUuHFjn+XUnTVXXnmldu7ceUZ748aNFRYWpmbNmunEiRPavHmz9zF2796tH3744byetz/CwsJ8fvUXwIUhoACVUFRUlFq1aqV58+YpLS1N0s+hZcuWLfr888+928qqZs2aGjJkiB544AG999572rFjhwYPHqygoP//VvLWW2/p6aef1tatW/XVV1/p5ZdfVklJic9poOzsbI0aNUq7d+/WggULNH36dN1///2SpCZNmujWW2/VwIEDtXjxYu3du1ebNm3S448/rrfffluS9NBDD2nDhg0aNmyYtm7dqqysLL3xxhsaMWKEJOnSSy9Vz549NXToUH300UfKzMzUnXfeeVFvqU5KStKRI0f03nvv6eDBg2ec1gJQNgQUoJLq3LmzTp486Q0jderUUfPmzVW/fn01a9bsvOs+8cQT6tixo3r37q2uXbuqQ4cOat26tbe9du3aWrx4sa655ho1a9ZMzz33nBYsWKDLLrvM22fgwIE6duyY/uVf/kXDhg3TiBEjdNddd3nbZ82apYEDB2r06NG69NJL1bt3b3300UdKSEiQ9PO1IGvWrFFWVpauvvpqXXHFFfrP//xP73Uhp2okJCSoU6dO6tevn+666y41aNDgvJ/3r0lNTdU999yj/v37q379+poyZcpFeyygKvCYPyeVASBA0tLS1KpVK7+/ch5A1cQRFAAA4BwCCoAqZ/78+T63KJ++nH4qCkD54RQPgCrn8OHD+vbbb8/aFhoaqsTExN94RAB+iYACAACcwykeAADgHAIKAABwDgEFAAA4h4ACAACcQ0ABAADOIaAAAADnEFAAAIBz/h8UQKfZ8hRf6wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.分组 ，对 组内 求 count 均值，画 柱状图\n",
    "import pandas as pd\n",
    "# 假设 g 是一个 GroupBy 对象，需要计算均值\n",
    "def mean_numeric_columns(group):\n",
    "    numeric_columns = group.select_dtypes(include='number')  # 选择数值型列\n",
    "    return numeric_columns.mean()\n",
    "\n",
    "all_df.groupby('windspeed_int').apply(mean_numeric_columns)['count'].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "44e3bfed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "重点查看 独热编码后 新属性列的名称all_df.columns\n",
      "Index(['datetime', 'holiday', 'workingday', 'temp', 'atemp', 'humidity',\n",
      "       'windspeed', 'casual', 'registered', 'count', 'trainortest', 'date',\n",
      "       'month_num', 'month', 'weekday_num', 'weekday', 'hour', 'hour_section',\n",
      "       'hour_weekday_section', 'hour_workingday', 'temp_int', 'humidity_int',\n",
      "       'windspeed_int', 'season_1', 'season_2', 'season_3', 'season_4',\n",
      "       'weather_1', 'weather_2', 'weather_3', 'weather_4'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# #五、特征工程\n",
    "# # 做完这些分析，准备做特征工程，比如归一化，缺失值填充，构造新的特征等。\n",
    "#一.对于 连续型数据 的数据预处理 使用特征缩放的方式\n",
    "#1特征缩放：\n",
    "#（1）(x - x_min)/(x_max-x_min)  归一化 将 原先的属性 调整成 0-1之间。\n",
    "#（2）(x - 均值mu)/(标准差sigma)  标准化  将 原先的属性 调整成 符合标准正态分布的形式 (-1,1) 根据3sigma原则，99.7%的数据在(-3,3)\n",
    "# all_df.info()\n",
    "#\n",
    "#二.对于 离散型数据 的数据预处理 使用 独热编码 的方式\n",
    "# #使用pandas做onehot操作 需要让大家进行优化处理，修改为使用sklearn.preprocision 中的OneHotEncoder进行处理\n",
    "# #对 季节 season 和 天气恶劣程度 weather 进行独热编码\n",
    "all_df=pd.get_dummies(all_df,columns=['season','weather'])\n",
    "print(\"重点查看 独热编码后 新属性列的名称all_df.columns\")\n",
    "print(all_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a6f963a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将 风速，温度，湿度 数据标准化处理\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss=StandardScaler()\n",
    "all_df['windspeed_scaled']=ss.fit_transform(all_df[['windspeed']])#以数据帧方式 获取某列数据\n",
    "all_df['temp_scaled']=ss.fit_transform(all_df[['temp']])#以数据帧方式 获取某列数据\n",
    "all_df['humidity_scaled']=ss.fit_transform(all_df[['humidity']])#以数据帧方式 获取某列数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "339fc1ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3236     0.882641\n",
       "1067    -0.830346\n",
       "3245    -0.830346\n",
       "2300     0.882641\n",
       "4716    -0.218624\n",
       "           ...   \n",
       "9983    -0.708166\n",
       "6157     0.760461\n",
       "10221   -0.830346\n",
       "1025     1.617365\n",
       "168     -0.708166\n",
       "Name: windspeed_scaled, Length: 10739, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df['windspeed_scaled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "46c32482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3236     1.101872\n",
       "1067    -1.217726\n",
       "3245     0.785564\n",
       "2300     1.101872\n",
       "4716     0.152946\n",
       "           ...   \n",
       "9983    -0.690544\n",
       "6157    -1.744907\n",
       "10221   -1.006853\n",
       "1025    -1.534035\n",
       "168     -1.744907\n",
       "Name: temp_scaled, Length: 10739, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df['temp_scaled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ffe19ba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3236     0.621106\n",
       "1067     0.153342\n",
       "3245     0.829001\n",
       "2300    -1.977584\n",
       "4716    -0.730213\n",
       "           ...   \n",
       "9983    -0.314422\n",
       "6157    -1.146003\n",
       "10221    1.400713\n",
       "1025     0.673080\n",
       "168      0.621106\n",
       "Name: humidity_scaled, Length: 10739, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df['humidity_scaled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0f9b5dfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['datetime', 'holiday', 'workingday', 'temp', 'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count', 'trainortest', 'date', 'month_num', 'weekday_num', 'hour_section', 'temp_int', 'humidity_int', 'windspeed_int', 'season_1', 'season_2', 'season_3', 'season_4', 'weather_1', 'weather_2', 'weather_3', 'weather_4', 'windspeed_scaled', 'temp_scaled', 'humidity_scaled', 'month_April', 'month_August', 'month_December', 'month_February', 'month_January', 'month_July', 'month_June', 'month_March', 'month_May', 'month_November', 'month_October', 'month_September', 'hour_0', 'hour_1', 'hour_2', 'hour_3', 'hour_4', 'hour_5', 'hour_6', 'hour_7', 'hour_8', 'hour_9', 'hour_10', 'hour_11', 'hour_12', 'hour_13', 'hour_14', 'hour_15', 'hour_16', 'hour_17', 'hour_18', 'hour_19', 'hour_20', 'hour_21', 'hour_22', 'hour_23', 'weekday_Friday', 'weekday_Monday', 'weekday_Saturday', 'weekday_Sunday', 'weekday_Thursday', 'weekday_Tuesday', 'weekday_Wednesday', 'hour_workingday_0_0', 'hour_workingday_0_1', 'hour_workingday_10_0', 'hour_workingday_10_1', 'hour_workingday_11_0', 'hour_workingday_11_1', 'hour_workingday_12_0', 'hour_workingday_12_1', 'hour_workingday_13_0', 'hour_workingday_13_1', 'hour_workingday_14_0', 'hour_workingday_14_1', 'hour_workingday_15_0', 'hour_workingday_15_1', 'hour_workingday_16_0', 'hour_workingday_16_1', 'hour_workingday_17_0', 'hour_workingday_17_1', 'hour_workingday_18_0', 'hour_workingday_18_1', 'hour_workingday_19_0', 'hour_workingday_19_1', 'hour_workingday_1_0', 'hour_workingday_1_1', 'hour_workingday_20_0', 'hour_workingday_20_1', 'hour_workingday_21_0', 'hour_workingday_21_1', 'hour_workingday_22_0', 'hour_workingday_22_1', 'hour_workingday_23_0', 'hour_workingday_23_1', 'hour_workingday_2_0', 'hour_workingday_2_1', 'hour_workingday_3_0', 'hour_workingday_3_1', 'hour_workingday_4_0', 'hour_workingday_4_1', 'hour_workingday_5_0', 'hour_workingday_5_1', 'hour_workingday_6_0', 'hour_workingday_6_1', 'hour_workingday_7_0', 'hour_workingday_7_1', 'hour_workingday_8_0', 'hour_workingday_8_1', 'hour_workingday_9_0', 'hour_workingday_9_1', 'hour_weekday_section_0', 'hour_weekday_section_1', 'hour_weekday_section_2', 'hour_weekday_section_3', 'hour_weekday_section_4', 'hour_weekday_section_5', 'hour_weekday_section_6', 'hour_weekday_section_7']\n"
     ]
    }
   ],
   "source": [
    "# # 独热编码处理\n",
    "#\n",
    "all_df=pd.get_dummies(all_df,columns=['month'])\n",
    "all_df=pd.get_dummies(all_df,columns=['hour'])\n",
    "all_df=pd.get_dummies(all_df,columns=['weekday'])\n",
    "all_df=pd.get_dummies(all_df,columns=['hour_workingday'])\n",
    "all_df=pd.get_dummies(all_df,columns=['hour_weekday_section'])\n",
    "print(all_df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ac34e504",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #保存 数据 到 csv 文件\n",
    "all_df.to_csv('20210526.csv')\n",
    "# all_df.to_excel('20210526.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d45e1132",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #挑选出 需要使用 的 属性，保存在列表中\n",
    "feature_columns=['season_1', 'season_2', 'season_3', 'season_4', 'weather_1', 'weather_2', 'weather_3', 'weather_4', 'windspeed_scaled', 'temp_scaled', 'humidity_scaled', 'month_April', 'month_August', 'month_December', 'month_February', 'month_January', 'month_July', 'month_June', 'month_March', 'month_May', 'month_November', 'month_October', 'month_September', 'hour_0', 'hour_1', 'hour_2', 'hour_3', 'hour_4', 'hour_5', 'hour_6', 'hour_7', 'hour_8', 'hour_9', 'hour_10', 'hour_11', 'hour_12', 'hour_13', 'hour_14', 'hour_15', 'hour_16', 'hour_17', 'hour_18', 'hour_19', 'hour_20', 'hour_21', 'hour_22', 'hour_23', 'weekday_Friday', 'weekday_Monday', 'weekday_Saturday', 'weekday_Sunday', 'weekday_Thursday', 'weekday_Tuesday', 'weekday_Wednesday', 'hour_workingday_0_0', 'hour_workingday_0_1', 'hour_workingday_10_0', 'hour_workingday_10_1', 'hour_workingday_11_0', 'hour_workingday_11_1', 'hour_workingday_12_0', 'hour_workingday_12_1', 'hour_workingday_13_0', 'hour_workingday_13_1', 'hour_workingday_14_0', 'hour_workingday_14_1', 'hour_workingday_15_0', 'hour_workingday_15_1', 'hour_workingday_16_0', 'hour_workingday_16_1', 'hour_workingday_17_0', 'hour_workingday_17_1', 'hour_workingday_18_0', 'hour_workingday_18_1', 'hour_workingday_19_0', 'hour_workingday_19_1', 'hour_workingday_1_0', 'hour_workingday_1_1', 'hour_workingday_20_0', 'hour_workingday_20_1', 'hour_workingday_21_0', 'hour_workingday_21_1', 'hour_workingday_22_0', 'hour_workingday_22_1', 'hour_workingday_23_0', 'hour_workingday_23_1', 'hour_workingday_2_0', 'hour_workingday_2_1', 'hour_workingday_3_0', 'hour_workingday_3_1', 'hour_workingday_4_0', 'hour_workingday_4_1', 'hour_workingday_5_0', 'hour_workingday_5_1', 'hour_workingday_6_0', 'hour_workingday_6_1', 'hour_workingday_7_0', 'hour_workingday_7_1', 'hour_workingday_8_0', 'hour_workingday_8_1', 'hour_workingday_9_0', 'hour_workingday_9_1', 'hour_weekday_section_0', 'hour_weekday_section_1', 'hour_weekday_section_2', 'hour_weekday_section_3', 'hour_weekday_section_4', 'hour_weekday_section_5', 'hour_weekday_section_6', 'hour_weekday_section_7']\n",
    "# #获取 x_train,x_test,y_train,y_test\n",
    "x_train=all_df.loc[all_df['trainortest']=='train',feature_columns]\n",
    "x_test=all_df.loc[all_df['trainortest']=='test',feature_columns]\n",
    "y_train=all_df.loc[all_df['trainortest']=='train','count']\n",
    "y_test=all_df.loc[all_df['trainortest']=='test','count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7330b0ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.830346</td>\n",
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       "      <td>...</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.025738</td>\n",
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       "      <td>...</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8053 rows × 110 columns</p>\n",
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      ],
      "text/plain": [
       "      season_1  season_2  season_3  season_4  weather_1  weather_2  weather_3  \\\n",
       "3236     False     False      True     False       True      False      False   \n",
       "1067      True     False     False     False       True      False      False   \n",
       "3245     False     False      True     False       True      False      False   \n",
       "2300     False      True     False     False       True      False      False   \n",
       "4716     False     False     False      True       True      False      False   \n",
       "...        ...       ...       ...       ...        ...        ...        ...   \n",
       "6371      True     False     False     False      False      False       True   \n",
       "5632      True     False     False     False      False      False       True   \n",
       "5250     False     False     False      True       True      False      False   \n",
       "3967     False     False      True     False       True      False      False   \n",
       "81        True     False     False     False       True      False      False   \n",
       "\n",
       "      weather_4  windspeed_scaled  temp_scaled  ...  hour_workingday_9_0  \\\n",
       "3236      False          0.882641     1.101872  ...                False   \n",
       "1067      False         -0.830346    -1.217726  ...                False   \n",
       "3245      False         -0.830346     0.785564  ...                False   \n",
       "2300      False          0.882641     1.101872  ...                False   \n",
       "4716      False         -0.218624     0.152946  ...                False   \n",
       "...         ...               ...          ...  ...                  ...   \n",
       "6371      False          0.270919    -0.690544  ...                False   \n",
       "5632      False         -0.830346    -1.534035  ...                False   \n",
       "5250      False         -0.830346    -1.006853  ...                False   \n",
       "3967      False          0.270919    -0.374235  ...                False   \n",
       "81        False          0.025738    -1.323162  ...                False   \n",
       "\n",
       "      hour_workingday_9_1  hour_weekday_section_0  hour_weekday_section_1  \\\n",
       "3236                False                   False                   False   \n",
       "1067                False                    True                   False   \n",
       "3245                False                    True                   False   \n",
       "2300                False                   False                   False   \n",
       "4716                False                   False                   False   \n",
       "...                   ...                     ...                     ...   \n",
       "6371                False                   False                   False   \n",
       "5632                False                   False                   False   \n",
       "5250                False                   False                   False   \n",
       "3967                False                   False                    True   \n",
       "81                  False                   False                   False   \n",
       "\n",
       "      hour_weekday_section_2  hour_weekday_section_3  hour_weekday_section_4  \\\n",
       "3236                   False                    True                   False   \n",
       "1067                   False                   False                   False   \n",
       "3245                   False                   False                   False   \n",
       "2300                   False                    True                   False   \n",
       "4716                    True                   False                   False   \n",
       "...                      ...                     ...                     ...   \n",
       "6371                   False                    True                   False   \n",
       "5632                   False                    True                   False   \n",
       "5250                   False                    True                   False   \n",
       "3967                   False                   False                   False   \n",
       "81                      True                   False                   False   \n",
       "\n",
       "      hour_weekday_section_5  hour_weekday_section_6  hour_weekday_section_7  \n",
       "3236                   False                   False                   False  \n",
       "1067                   False                   False                   False  \n",
       "3245                   False                   False                   False  \n",
       "2300                   False                   False                   False  \n",
       "4716                   False                   False                   False  \n",
       "...                      ...                     ...                     ...  \n",
       "6371                   False                   False                   False  \n",
       "5632                   False                   False                   False  \n",
       "5250                   False                   False                   False  \n",
       "3967                   False                   False                   False  \n",
       "81                     False                   False                   False  \n",
       "\n",
       "[8053 rows x 110 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0a620019",
   "metadata": {},
   "outputs": [
    {
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "</table>\n",
       "<p>2686 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season_1  season_2  season_3  season_4  weather_1  weather_2  \\\n",
       "3839      False     False      True     False      False       True   \n",
       "5605       True     False     False     False       True      False   \n",
       "4698      False     False     False      True       True      False   \n",
       "5698       True     False     False     False       True      False   \n",
       "553        True     False     False     False       True      False   \n",
       "...         ...       ...       ...       ...        ...        ...   \n",
       "9983      False     False     False      True      False       True   \n",
       "6157       True     False     False     False       True      False   \n",
       "10221     False     False     False      True       True      False   \n",
       "1025       True     False     False     False       True      False   \n",
       "168        True     False     False     False      False       True   \n",
       "\n",
       "       weather_3  weather_4  windspeed_scaled  temp_scaled  ...  \\\n",
       "3839       False      False         -1.565070     0.680127  ...   \n",
       "5605       False      False          0.882641    -0.479672  ...   \n",
       "4698       False      False         -1.565070    -0.479672  ...   \n",
       "5698       False      False         -0.218624    -0.374235  ...   \n",
       "553        False      False          0.270919    -1.217726  ...   \n",
       "...          ...        ...               ...          ...  ...   \n",
       "9983       False      False         -0.708166    -0.690544  ...   \n",
       "6157       False      False          0.760461    -1.744907  ...   \n",
       "10221      False      False         -0.830346    -1.006853  ...   \n",
       "1025       False      False          1.617365    -1.534035  ...   \n",
       "168        False      False         -0.708166    -1.744907  ...   \n",
       "\n",
       "       hour_workingday_9_0  hour_workingday_9_1  hour_weekday_section_0  \\\n",
       "3839                 False                False                   False   \n",
       "5605                 False                False                   False   \n",
       "4698                 False                False                   False   \n",
       "5698                 False                False                   False   \n",
       "553                  False                False                   False   \n",
       "...                    ...                  ...                     ...   \n",
       "9983                 False                False                   False   \n",
       "6157                 False                False                   False   \n",
       "10221                False                False                   False   \n",
       "1025                 False                False                    True   \n",
       "168                  False                False                   False   \n",
       "\n",
       "       hour_weekday_section_1  hour_weekday_section_2  hour_weekday_section_3  \\\n",
       "3839                    False                   False                   False   \n",
       "5605                    False                   False                   False   \n",
       "4698                    False                   False                    True   \n",
       "5698                    False                    True                   False   \n",
       "553                     False                   False                   False   \n",
       "...                       ...                     ...                     ...   \n",
       "9983                     True                   False                   False   \n",
       "6157                    False                   False                   False   \n",
       "10221                   False                   False                   False   \n",
       "1025                    False                   False                   False   \n",
       "168                     False                   False                   False   \n",
       "\n",
       "       hour_weekday_section_4  hour_weekday_section_5  hour_weekday_section_6  \\\n",
       "3839                    False                   False                   False   \n",
       "5605                    False                   False                    True   \n",
       "4698                    False                   False                   False   \n",
       "5698                    False                   False                   False   \n",
       "553                     False                    True                   False   \n",
       "...                       ...                     ...                     ...   \n",
       "9983                    False                   False                   False   \n",
       "6157                    False                   False                    True   \n",
       "10221                   False                    True                   False   \n",
       "1025                    False                   False                   False   \n",
       "168                     False                    True                   False   \n",
       "\n",
       "       hour_weekday_section_7  \n",
       "3839                     True  \n",
       "5605                    False  \n",
       "4698                    False  \n",
       "5698                    False  \n",
       "553                     False  \n",
       "...                       ...  \n",
       "9983                    False  \n",
       "6157                    False  \n",
       "10221                   False  \n",
       "1025                    False  \n",
       "168                     False  \n",
       "\n",
       "[2686 rows x 110 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "afc3cafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3236    523\n",
       "1067      9\n",
       "3245      6\n",
       "2300    564\n",
       "4716    179\n",
       "       ... \n",
       "6371    128\n",
       "5632    190\n",
       "5250    165\n",
       "3967    409\n",
       "81       97\n",
       "Name: count, Length: 8053, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "31dc4e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3839     139\n",
       "5605     314\n",
       "4698     181\n",
       "5698     156\n",
       "553        1\n",
       "        ... \n",
       "9983     680\n",
       "6157      80\n",
       "10221     68\n",
       "1025      34\n",
       "168        9\n",
       "Name: count, Length: 2686, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "24e0e6d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7690910222391896\n",
      "{'alpha': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# #六、模型创建及评测¶\n",
    "from sklearn.linear_model import Lasso,Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# #使用 带L1正则化项 的线性回归 Lasso回归\n",
    "# #使用 带L2正则化项 的线性回归 Ridge回归\n",
    "la = Lasso()\n",
    "pg={\n",
    "    'alpha':[0.1,0.5,1,2,10]\n",
    "    # ,'gamma':[1,5,9,10]\n",
    "}\n",
    "gs = GridSearchCV(la,param_grid=pg,cv=5)\n",
    "gs.fit(x_train,y_train)\n",
    "print(gs.best_score_)\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a4822b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score 0.7684358555257697\n"
     ]
    }
   ],
   "source": [
    "#带入 最优超级参数，重新使用完整的训练集 训练模型， 再使用测试集合进行测试\n",
    "la_best_alpha =Lasso(alpha=0.1)\n",
    "la_best_alpha.fit(x_train,y_train)\n",
    "score=la_best_alpha.score(x_test,y_test)\n",
    "print('score',score)\n",
    "#\n",
    "# rg = Ridge()\n",
    "# pg={\n",
    "#     'alpha':[0.1,0.5,1,2,10]\n",
    "# }\n",
    "# gs = GridSearchCV(rg,param_grid=pg,cv=5)\n",
    "# gs.fit(x_train,y_train)\n",
    "# print(gs.best_score_)\n",
    "# print(gs.best_params_)\n",
    "#带入 最优超级参数，重新使用完整的训练集 训练模型， 再使用测试集合进行测试(大家自己补充一下代码)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b4bc645",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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